# Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive TransportClick to copy article linkArticle link copied!

- Siyan LiuSiyan LiuDepartment of Chemical & Petroleum Engineering, University of Kansas, Lawrence, Kansas 66045, United StatesComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United StatesMore by Siyan Liu
- Reza Barati
*****Reza BaratiDepartment of Chemical & Petroleum Engineering, University of Kansas, Lawrence, Kansas 66045, United States*****Email: [email protected]More by Reza Barati - Chi ZhangChi ZhangDepartment of Meteorology and Geophysics, Institute of Meteorology and Geophysics, University of Vienna, Universität Wien, UZA II, Josef-Holaubek-Platz 2, Wien 1090, AustriaMore by Chi Zhang
- Mohammad KazemiMohammad KazemiDepartment of Physics and Engineering, Slippery Rock University, Slippery Rock, Pennsylvania 16057, United StatesMore by Mohammad Kazemi

## Abstract

In this paper, we propose a modeling framework for pore-scale fluid flow and reactive transport based on a coupled lattice Boltzmann model (LBM). We develop a modeling interface to integrate the LBM modeling code parallel lattice Boltzmann solver and the PHREEQC reaction solver using multiple flow and reaction cell mapping schemes. The major advantage of the proposed workflow is the high modeling flexibility obtained by coupling the geochemical model with the LBM fluid flow model. Consequently, the model is capable of executing one or more complex reactions within desired cells while preserving the high data communication efficiency between the two codes. Meanwhile, the developed mapping mechanism enables the flow, diffusion, and reactions in complex pore-scale geometries. We validate the coupled code in a series of benchmark numerical experiments, including 2D single-phase Poiseuille flow and diffusion, 2D reactive transport with calcite dissolution, as well as surface complexation reactions. The simulation results show good agreement with analytical solutions, experimental data, and multiple other simulation codes. In addition, we design an AI-based optimization workflow and implement it on the surface complexation model to enable increased capacity of the coupled modeling framework. Compared to the manual tuning results proposed in the literature, our workflow demonstrates fast and reliable model optimization results without incorporating pre-existing domain knowledge.

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### License Summary*

You are free to share(copy and redistribute) this article in any medium or format within the parameters below:

Creative Commons (CC): This is a Creative Commons license.

Attribution (BY): Credit must be given to the creator.

Non-Commercial (NC): Only non-commercial uses of the work are permitted.

No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited.

*Disclaimer

This summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials.

### License Summary*

You are free to share(copy and redistribute) this article in any medium or format within the parameters below:

Creative Commons (CC): This is a Creative Commons license.

Attribution (BY): Credit must be given to the creator.

Non-Commercial (NC): Only non-commercial uses of the work are permitted.

No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited.

*Disclaimer

This summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials.

### License Summary*

Creative Commons (CC): This is a Creative Commons license.

Attribution (BY): Credit must be given to the creator.

Non-Commercial (NC): Only non-commercial uses of the work are permitted.

*Disclaimer

### License Summary*

Creative Commons (CC): This is a Creative Commons license.

Attribution (BY): Credit must be given to the creator.

Non-Commercial (NC): Only non-commercial uses of the work are permitted.

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## 1. Introduction

_{2}injection and sequestration (2) are two typical subsurface reactive transport-associated scientific and engineering problems that involve complex physio-chemical processes. Meanwhile, they are by nature highly uncertain processes due to the high complexity level of geological settings, which brings more challenges to conducting high-resolution mechanistic modeling studies compared to the reactive transport problems that happen in an artificial structure or domain such as reaction beds or combustion engine cylinders. Take advanced waterflooding or low-salinity waterflooding EOR modeling as an example, multiple underlying mechanisms are potentially working together and affect the rock/mineral surface wettability, and hence improve the recovery, but the actual underlying mechanisms are not fully understood and still debatable. (3−5) Meanwhile, the modeling approaches to simulate this process have simplified the problems without incorporating all physio-chemical processes in the pore-scale high-resolution fashion. (6−9)

_{2}reactive transport, (28) reactions between immiscible fluids, (29) and convection and heat transfer-associated problems. (30,31) More studies for the LBM multicomponent reactive transport models can be found in the review by Yoon et al. (32)

## 2. Methodology

### 2.1. Overview of the Proposed Modeling Workflow

### 2.2. LBM Fluid Flow Model

#### 2.2.1. LBM Fundamentals

*f*

_{i}

^{EQ}represents the particle distribution function at equilibrium. By assuming the existence of a local attractor and considering its impact on the collision process, the BGK model linearizes the collision term to reach local equilibrium using a fixed relaxation time and fluid viscosity. The BGK model utilizes single relaxation time τ in the evolution function (eq 9). Future research and model development will include the two-relaxation time or multi-relaxation time models for improved model stability purpose.

_{LB}(

**,**

*x**t*) is obtained by summing the particle distribution function on all points within the given grid block

*f*

_{i}(

**,**

*x**t*) represents the particle distribution function for velocity

*i*. Meanwhile, the lattice pressure

*P*

_{LB}is recovered by

*C*

_{s}is a constant value for the lattice speed of sound, (87) which varies for different models. For instance, in the D2Q9 model, as mentioned, ${C}_{\mathrm{s}}=1/\sqrt{3}$, and the lattice velocity vector

*u*_{LB}(

**,**

*x**t*) is obtained by

*e*_{i}represents the relative locations of the points within the specific grid block pointing to the adjacent grids, whose values are 0, 1, or −1;

*i*is the index for the points within the grid block for the given model;

**stands for the location of the grid block in the 2D or 3D simulation domain; and**

*x**t*describes the time step in the simulation for time-dependent particle distribution function evolution.

*f*

_{i}

^{eq}is given based on the two local macroscopic lattice properties: lattice velocity

**(**

*u***,**

*x**t*) and lattice density ρ

_{LB}(

**,**

*x**t*). To ensure the mass and momentum conservation during the collision and streaming process, specific equations and weights ω

_{i}are required for different models. (88,89) In the D2Q9 model, the equilibrium distribution functions are written as

_{i}represents a series of weights of a given lattice model to maintain the isotropy of the fourth-order tensor of velocities and the Galilean invariance. (88) The weight distribution of the points within a single lattice grid for three widely used lattice models is described in Table 1.

Model | center | horizontal/vertical | diagonal |
---|---|---|---|

D1Q3 | 2/3 | 1/6 | 0 |

D2Q9 | 4/9 | 1/9 | 1/36 |

D3Q19 | 1/3 | 1/18 | 1/36 |

_{0}= 4/9 in eq 5 describes the equilibrium function for the center point within the grid block, where the rest of the 8 points are given by eq 6 with ω

_{i}= 1/36 or 1/9 depending on the geometrical position of the point with respect to the grid block.

*e*_{i}is determined by

_{τ}is the relaxation frequency defined by Ω

_{τ}= 1/τ; the relaxation time τ indicates the time used for the system to reach equilibrium during each streaming-collision cycle, which is associated with the lattice kinematic viscosity ν

_{LB}(88)

*f*

_{i}

^{eq}based on eqs 5 and 6 (for the D2Q9 model in this case). Next, the collision and streaming processes based on the evolution eq 9 are executed. Specifically, the local particle distribution function

*f*

_{i}(

**,**

*x**t*) is altered by the current equilibrium function

*f*

_{i}

^{eq}within the relaxation time τ described in the evolution function. Followed by the collision step, the streaming step simply transfers the updated distribution functions to adjacent nodes, and a full iteration is completed. Boundary treatments are executed after the full cycle if applicable.

#### 2.2.2. LBM Advection–Diffusion Model

*C*represents a scalar field for quantities that can be transported by the flow while diffuse based on the concentration or temperature gradient,

**stands for the flow or advection velocity vector,**

*u**D*is the diffusion coefficient, and

*q*is an optional source term which can be used to represent the locally destroyed or produced chemical species (if

*C*is for chemical concentration field) or represents the consumption or production of heat (if

*C*is the temperature field). These behaviors can be attributed to the mechanisms such as chemical reactions. In terms of the diffusion process, the associated LBM formulation can be written as the following evolution function

*Q*

_{i}(

*x*,

*t*) represents the same meaning as the source term

*q*in eq 11 in a “discretized” form with respective to lattice space and time. For example, if we have an additional reaction solver to “produce” or “destroy” chemical species in each lattice grid at each lattice time step, this would be added in the “

*Q*” source term of the LBM evolution equations to incorporate the advection–diffusion process.

*C*is recovered by the equation below similar to the lattice density ρ in the BGK LBM flow model

*D*is obtained by the following equation in light of the lattice kinematic viscosity (ν) in the BGK LBM flow model

*C*with another fluid flow lattice with a unified solving time step.

#### 2.2.3. PALABOS LBM Solver

### 2.3. Geochemical Reaction Model

#### 2.3.1. Geochemical Reaction Solver

#### 2.3.2. PHREEQCRM Geochemical Reaction Models

### 2.4. Model Coupling Interface

#### 2.4.1. Data Pre-/Post-Processing and Script Input

#### 2.4.2. Model Initialization

#### 2.4.3. Cell Mapping Model

(1) | Fluid cells: cells represent the void space in the porous media | ||||

(2) | Fluid interface cells: cells belong to “fluid cells” and are positioned at the outer surface layer cells of the “fluid cells” | ||||

(3) | Solid cells: cells without any fluid flow and reaction enabled, defined from the initial input geometry | ||||

(4) | Solid interface cells: cells belong to and occupy the outer surface layer of the “solid cells” |

fluid cells | fluid interface cells | solid cells | solid interface cells | |
---|---|---|---|---|

fluid flow | yes | yes | no | no |

diffusion | yes | yes | no | yes |

equilibrium reaction | yes | yes | no | yes |

kinetic reaction | no | no | no | yes |

dissolution | no | no | no | yes |

precipitation | no | yes | no | yes |

surface complexation | no | no | no | yes |

^{a}

The solid cells are inactive, whereas fluid cells only host advection–diffusion–reaction in the aqueous phase, and the interface cells are located at the boundary of the solid–fluid interfaces, hosting the fluid–solid interaction-associated processes except for the fluid flow.

#### 2.4.4. PALABOS Data Processor

#### 2.4.5. PHREEQCRM Basic Function Feature

*d*

_{s}is the distance between the slipping plane and the stern layer in the electrical double layer model, ψ stands for the surface potential, and κ represents the inverse of the Debye length which is obtained by

*N*

_{A}is the Avogadro’s number;

*e*is the charge of an electron;

*I*represents the ionic strength; ϵ

_{r}and ϵ

_{0}are the temperature-dependent relative permittivity of the solution and the vacuum permittivity (8.8541878128

^{–12}F m

^{–1}), respectively;

*k*

_{b}= 1.38064852

^{–23}J K

^{–1}is the Boltzmann constant; and

*T*is the temperature in Kelvin.

### 2.5. AI-Assisted SCM Model Optimizations

*K*) based on the experimental measured zeta potentials (ζ). To validate the SCM model integration for the coupling code, an SCM model based on the experimental data by Tetteh et al. (105,106) is developed and optimized for the brine/calcite interface. Instead of estimating and calibrating the ζ within a bulk fluid solution, a more realistic 2D simulation domain is created with a centered spherical calcite grain surrounded by solutions with various salinities. Then, the PALABOS–PHREEQCRM coupled solver is used to obtain the surface properties such as ζ, surface potentials, and Debye length. The simulated ζ over the surface of the calcite grain is averaged and used as the target to compare with the experimentally measured ones and re-adjust the reaction constants if they do not match. Since the directions for adjusting the sets of reaction constants are unknown, and each one of the numerical simulations takes time, an optimization framework is developed to assist the parameter adjustment tasks. The framework connects the geochemically coupled PALABOS–PHREEQCRM simulator with a multi-layer perceptron (MLP) neural network (NN). A series of pre-simulated model parameters with the calculated ζ are prepared as the pre-train data for NN. The NN and simulator communicate with each other interactively to update the log

*K*and match the target ζ as much as possible. The conventional mean squared error (MSE) loss function and the stochastic gradient descent (SGD) optimizer are used during the interactive training and optimization process.

## 3. Model Validation and Results

### 3.1. LBM Single-Phase Fluid Flow in a 2D Channel

^{–8}[Pa]) is applied across the channel with an inlet on the left and an outlet on the right. The no-flow boundary conditions are imposed on the top and bottom walls. Water is assumed as the fluid used in this experiment at a standard temperature of 25 °C, and it has a density of $997\phantom{\rule{.25em}{0ex}}[\mathrm{k}\mathrm{g}/{\mathrm{m}}^{3}]$, viscosity of 8.891 × 10

^{–4}[Pa·s], and kinematic viscosity of $8.917\times {10}^{-7}\phantom{\rule{.25em}{0ex}}[{\mathrm{m}}^{2}/\mathrm{s}]$. The fluid flow is assumed to be a steady-state laminar flow, and the viscous dissipation and gravity effects are neglected.

*u*(

*y*)

*y*is the coordinate across the width of the channel,

*H*is the width of the channel, μ is the fluid viscosity, and d

*p*/d

*x*represents the assigned pressure gradient along the length of the channel. The average velocity is computed as

^{–8}[m/s] and the average velocity is 1.24970 × 10

^{–8}[m/s]. The same physical properties are applied and converted to lattice units, and the LBM simulation is conducted. The pressure gradient is represented by the lattice density gradient map shown in Figure 6.

^{–8}[m/s]) and average 1.24371 × 10

^{–8}[m/s] velocities after unit conversion agree with the analytical solutions with minor errors (0.48 and 0.54%, respectively). Furthermore, additional 10 locations across the width of the channel from the LBM simulated velocity profile are extracted, and they show good agreement with the analytical solutions, as shown in Figure 8.

### 3.2. LBM Diffusion in a 2D Channel

*c*

_{0}= 1.0 is imposed. The right-hand side of the channel is assumed to be infinitely long (

*c*

_{inf}= 0), and the boundary conditions and boundary effects are not considered for top and bottom walls.

*D*; and (4) the flow is one-dimensional Fick’s diffusion with a constant diffusion coefficient D = 10 × 10

^{–6}[m/s]. The analytical solution of the concentration profiles is solved from Fick’s second law

*c*

_{o}is the constant concentration (

*c*

_{o}= 1) on the left terminal,

*D*represents the diffusion coefficient,

*t*is the time,

*x*is the location from the left terminal, erfc stands for the complementary error function, and

*c*(

*x*,

*t*) describes the concentration of the distance

*x*from the left terminal at time

*t*. 7 time points (

*t*= 1, 20, 50, 100, 150, 200, 250 s) are assigned as the testing time for the concentration profile comparison evaluations.

*t*= 1 s) from the LBM simulation. The 1D concentration profile is extracted from the center line across the domain as shown in Figure 10 at the given time steps.

*R*

^{2}) which are defined in Appendix A. The evaluation results are shown in Table 3. The LBM simulated concentration profiles are in very good agreement with the analytical solutions at all time points except at

*t*= 1 s. This can be attributed to the fluctuations at the beginning of the simulation, and the error becomes significantly smaller as the time steps go by.

MAE error | MSE error | R^{2} score | |
---|---|---|---|

T = 1 s | 0.0030 | 0.0003 | 0.9772 |

T = 20 s | 0.0006 | 2.1829 × 10^{–6} | 0.9999 |

T = 50 s | 0.0004 | 5.4259 × 10^{–7} | 0.9999 |

T = 100 s | 0.0003 | 1.9075 × 10^{–7} | 0.9999 |

T = 150 s | 0.0003 | 1.1620 × 10^{–7} | 0.9999 |

T = 200 s | 0.0004 | 4.2959 × 10^{–7} | 0.9999 |

T = 250 s | 0.0009 | 3.1512 × 10^{–6} | 0.9999 |

### 3.3. Coupled Model for Advection–Diffusion–Reaction Transport in a 2D Channel with Calcite Dissolution Kinetics

^{–2}[mol/L], which results in a pH = 2.0. After the injection starts, the solution with a concentration of HCl is pushed into the channel continuously. Meanwhile, the irreversible heterogeneous reaction starts to dissolve the calcite based on the stoichiometric equation

^{+}concentration

*k*

_{H+}is the rate constant with units of $[\mathrm{m}\mathrm{o}\mathrm{l}\xb7{\mathrm{c}\mathrm{m}}^{-2}{\mathrm{s}}^{-1}]$, γ

_{H+}is the activity of H

^{+}with units of $[{\mathrm{c}\mathrm{m}}^{3}\xb7{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}]$, and the

*c*

_{H+}is the concentration of H

^{+}with units of $[\mathrm{m}\mathrm{o}\mathrm{l}\xb7{\mathrm{c}\mathrm{m}}^{-3}]$ or $[\mathrm{m}\mathrm{o}\mathrm{l}\xb7{\mathrm{L}}^{-1}]$.

parameters | symbol | value | units |
---|---|---|---|

fluid density | ρ | 1 | g cm^{–3} |

kinematic viscosity | ν | 10^{–2} | cm^{2} s^{–1} |

diffusion coefficient | D | 10^{–5} | cm^{2} s^{–1} |

inlet velocity | u_{in} | 0.12 | cm s^{–1} |

width of the channel | ω | 0.05 | cm |

specific grain reactive area | 200 | cm^{–1} | |

rate constant | kH^{+} | 10^{–4.05} | mol cm^{–2} s^{–1} |

activity coefficient | γH^{+} | 1000 | cm^{3} mol^{–1} |

inlet concentration (pH = 2) | C | 10^{–5} | mol cm^{–3} |

Reynolds number | Re = u_{in} ω ν^{–1} | 0.6 | |

Péclet number | Pe = u_{in} ω D^{–1} | 600 |

^{a}

Simplified from ref (107).

*R*) is calculated as

*A*stands for the reacting surface area of the calcite,

*c*

_{in}represents the universal inlet concentration from the boundary condition, and

*c*

_{out}is evaluated as the flux-weighted-average concentration across the outlet boundary

*Q*is obtained from the integration across the outlet

^{2+}concentration distribution at the same moment with respect to Figure 14A. The formation of the Ca

^{2+}concentration thin layer is observed around the grain as a result of the calcite dissolution, and the teardrop-shaped overall concentration pattern indicates the impacts of the flow velocity and direction.

^{+}concentration evolution at the start of the simulation (

*t*= 0.2 s) and after reaching a steady state at around

*t*= 1.5 s. The H

^{+}concentration distribution pattern demonstrates the H

^{+}consumption during the calcite dissolution processes, which matches the Ca

^{2+}concentration distribution pattern shown in Figure 14B.

^{+}concentration evolution at the right terminal of the channel directly from the concentration field from the LBM model is evaluated. Equation 27 is used to estimate the average reaction rate, and the results are compared according to Mollins et al. (107) The results are shown in Figure 16 for the outlet average effluent H

^{+}concentration evolution from the beginning of the simulation until the steady state at 1.5 s. The overall trend from PALABOS–PHREEQCRM agrees with the other five codes despite the initial bump. The small bump is caused by the equilibrium reaction results from the initialization step before the fluid starts to flow during the PHREEQCRM initialization stage. Specifically, we believe that the PHREEQCRM generates slightly higher H

^{+}concentrations (about 1.4%) at the assigned temperature of 40 °C; even we explicitly specified pH = 2.0. However, the effluent concentration reaches the desired equilibrium when the calcite dissolution kinetic reaction dominates the process, although the initial condition for the vortex method is different (pH = 7.0) compared to other methods, and the slightly different initial conditions do not affect the later and final solutions.

code | surface area (cm^{2}) | grain volume (cm^{3}) | average rate (mol cm^{–2} s^{–1}) |
---|---|---|---|

theoretical | 0.0628 | 3.14 × 10^{–4} | |

this study | 0.0628 | 3.14 × 10^{–4} | 4.59 × 10^{–8} |

OpenFOAM-DBS | 0.0628 | 3.14 × 10^{–4} | 4.18 × 10^{–8} |

lattice–Boltzmann | 0.0628 | 3.14 × 10^{–4} | 4.57 × 10^{–8} |

vortex | 0.0628 | 3.14 × 10^{–4} | 4.27 × 10^{–8} |

### 3.4. Static SCM Model and ANN Optimizations

brine | Ca^{2+} | Mg^{2+} | Na^{+} | K^{+} | Cl^{–} | SO_{4}^{2–} | IS (mol/L) | TDS, ppm |
---|---|---|---|---|---|---|---|---|

0.04-NaCl | 0 | 0 | 920 | 0 | 1418 | 0 | 0.04 | 2338 |

0.04-KCl | 0 | 0 | 0 | 1564 | 1418 | 0 | 0.04 | 2982 |

0.04-CaCl_{2} | 534 | 0 | 0 | 0 | 945 | 0 | 0.04 | 1480 |

0.04-MgCl_{2} | 0 | 324 | 0 | 0 | 945 | 0 | 0.04 | 1269 |

0.04-Na_{2}SO_{4} | 0 | 0 | 613 | 0 | 0 | 1281 | 0.04 | 1894 |

FWS | 11,000 | 2800 | 48,000 | 500 | 101,913 | 260 | 3.27 | 164,473 |

SWS | 2200 | 560 | 9600 | 100 | 20,383 | 52 | 0.65 | 32,895 |

LSW | 134 | 34 | 585 | 6 | 1243 | 3 | 0.04 | 2006 |

^{a}

The concentrations are in units of ppm prepared by Tetteh et al. (109) FWS: formation water; SWS: seawater; LSW: low salinity water.

^{2}) and calcite-specific surface area (1 m

^{2}/g) are deployed. Meanwhile, the

*P*

_{CO2}= 10

^{–3.4}atm condition is used in the SCM model with calcite as equilibrated phases.

reactions (brine/calcite interface) | Pokrovsky et al. (1999) | Wolters et al. (2008) | Hiorth et al. (2010) | Brady et al. (2012) | Brady et al. (2016) | Tetteh et al. (2020) |
---|---|---|---|---|---|---|

>CaOH + H^{+} ↔ >CaOH_{2}^{+} | 11.5 | 12.2 | 12.9 | 11.85 | 11.85 | 11.85 |

>CaOH_{2}^{+} + SO_{4}^{2–} ↔ CaSO_{4}^{–} + H_{2}O | 2.89 | 2.89 | 2.1 | 2.1 | 2.1 | 2.1 |

>CaOH + HCO_{3}^{–} ↔ >CaCO_{3}^{–} + H_{2}O | 5.6 | 4.9 | 3.32 | 5.8 | 4.28 | 5.8 |

>CO_{3}H ↔ >CO_{3}^{–} + H^{+} | –5.1 | –4.9 | –4.9 | –5.1 | –5.1 | –5.1 |

>CO_{3}H + Ca^{2+} ↔ >CO_{3}Ca^{+} + H^{+} | –1.7 | –2.8 | –3.16 | –2.6 | –2.6 | –4.4 |

>CO_{3}H + Mg^{2+} ↔ >CO_{3}Mg^{+} + H^{+} | –2.2 | –2.2 | –3.17 | –2.6 | –2.6 | –4.4 |

_{SIM}values for ANN pre-training. The data generated for pre-training containing about 20,000–40,000 samples “warm up” the ANN in the first few training epochs, which leads to a general desired direction to converge. Note that the ANN takes ζ

_{SIM}as inputs and predicts reaction constants log

*K*. The

*K*

_{i}is used to represent log

*K*values for simplicity. Furthermore, the interactive optimization is performed by (1) obtaining a set of

*K*

_{i}from ANN predictions; (2) assigning the ANN predicted

*K*

_{i}to PALABOS–PHREEQCRM and running coupled numerical simulations for all 8 solution cases under given temperature conditions; the resulting 8 ζ

_{SIM}values are used to compare the experimental measurements (ζ

_{EXP}) to compute the loss function; (3) representing the differences between ζ

_{SIM}and ζ

_{EXP}by the MSE loss

*n*is the number of ANN predictions, 8 is used since there are 8

*K*

_{i}, ${{\zeta}_{\mathrm{EXP}}}_{i}$ is the experimentally measured zeta potential and ${{\zeta}_{\mathrm{S}\mathrm{I}\mathrm{M}}}_{i}$ represents the numerical model simulated zeta potentials; and (4) updating the ANN weights based on the MSE loss; (5) using the updated ANN to predict a new set of

*K*

_{i}and enter the next iteration. The iteration is terminated until it converges where the MSE loss stops decreasing and reaches a “steady-state” condition.

*K*

_{i}obtained from the interactive optimizations for both 25 and 40 °C conditions after convergence.

reactions (brine/calcite interface) | 25 °C | 40 °C | Tetteh et al. (2020) |
---|---|---|---|

>CaOH + H^{+} ↔ >CaOH_{2}^{+} | 11.084 | 10.957 | 11.85 |

>CaOH_{2}^{+} + SO_{4}^{2–} ↔ CaSO_{4}^{–} + H_{2}O | 1.947 | 2.107 | 2.1 |

>CaOH + HCO_{3}^{–} ↔ >CaCO_{3}^{–} + H_{2}O | 5.567 | 5.485 | 5.8 |

>CO_{3}H ↔ >CO_{3}^{–} + H^{+} | –4.578 | –4.527 | –5.1 |

>CO_{3}H + Ca^{2+} ↔ >CO_{3}Ca^{+} + H^{+} | –3.614 | –3.454 | –4.4 |

>CO_{3}H + Mg^{2+} ↔ >CO_{3}Mg^{+} + H^{+} | –3.352 | –3.157 | –4.4 |

_{SIM}for FWS and LSW calculated during the ANN optimizations. A similar convergence pattern is observed with the MSE loss decay curves, which indicates that the ANN optimization framework is capable of minimizing the difference between SCM predicted ζ

_{SCM}and experimentally measured ζ

_{EXP}, with the reaction constants

*K*

_{i}adjusted automatically (Figure 20).

*K*

_{i}are used to calculate the final ζ

_{SIM}and compared to ζ

_{EXP}as well as results from Tetteh et al. (105)Figures 21 and 22 summarize the comparison for five single salt solutions and FWS/SWS/LSW cases. The ANN optimized results show good agreement with the tuned SCM model by Tetteh et al. (105) with a similar pattern. The predicted ζ

_{SIM}values are consistent with the experimental measurements for the five single salt solutions except for CaCl

_{2}and MgCl

_{2}, where slightly reverse surface charges are obtained. This can be attributed to the increased adsorption of Ca

^{2+}and increases >CO

_{3}Ca+ concentration due to calcite dissolution on the rock surface. (105) It is noticeable from the chart that the ζ

_{SIM}from ANN optimized models are slightly closer to the ζ

_{EXP}in most of the cases in Figure 21, indicating better

*K*

_{i}selection from ANN.

*R*

^{2}) are calculated for three groups: five single salt solutions, FWS/SWS/LSW, and all cases; the two evaluation metrics are defined in Appendix A. The result of the comparison is shown in Table 9, and it shows that the ANN generates better overall prediction accuracy with smaller MSE loss. In the meantime, ANN predicts much less error in the single salt solution cases. However, it underperforms slightly in FWS/SWS/LSW cases compared to the SCM models from Tetteh et al. (105) It is worth noting that no additional weights are added to the constructed loss function for each individual case during the optimizations. Thus, the ANN tends to reduce the overall error of the fitting process instead of focusing on a single comparison.

all cases | five single salt solution | FWS/SWS/LSW | ||||
---|---|---|---|---|---|---|

metrics | MSE | R^{2} | MSE | R^{2} | MSE | R^{2} |

ANN | 71.426 | 0.334 | 64.856 | 0.084 | 49.428 | 0.653 |

Tetteh et al. | 80.864 | 0.246 | 95.580 | –0.350 | 33.804 | 0.763 |

## 4. Conclusions, Discussion, and Future Work

_{2}sequestration. Meanwhile, the framework is flexible and can be broadly applied in different pore-scale reactive transport simulations. In this work, PHREEQC is used as the geochemical reaction solver due to its capabilities of solving multiple reaction types as well as being able to couple third-party codes for multiphysics modeling. One of the variants of PHREEQC called PHREEQCRM is selected for the model building since it exposes low-level API for coupling purposes and works as a C++ library. Meanwhile, by using PHREEQCRM, much faster reaction solving and data communication between the two packages are obtained. In addition, a cell-mapping mechanism is developed within the framework to handle different reaction types at various cells, which allows the implementation of complex reactions such as SCM models on a solid surface. To validate the integrated modeling framework, a series of numerical experiments are designed to demonstrate the capabilities of this framework, ranging from simple 2D channel fluid flow and 2D diffusion to a 2D reactive transport benchmark problem. Furthermore, SCMs are added to the tests and validated against the SCM models with experimental measurements proposed in the literature. Meanwhile, a workflow using ANN to automatically assist the SCM model optimization is proposed. It successfully improves the SCM model tuning while illustrating the great potential to optimize more complex scenarios or models.

*R*

^{2}≥ 0.9997), although minimal performance loss is observed at the beginning of the simulation due to the initial oscillations. After validating flow or diffusion-only scenarios, the experiment was inspired and designed by the reactive transport benchmark problem proposed by Molins et al. (107) and used the same initial conditions for the simulation. The time-dependent averaged effluent H

^{+}concentration is evaluated, and the averaged reaction/calcite dissolution rate as the metrics is estimated for comparison. The results demonstrate good agreement with the other five codes described in the benchmark problem. The last mode validation focuses on the SCM model verification for complex physical–chemical processes using an advanced waterflooding-associated mechanistic study as an example. In addition to the SCM model integration, an ANN-assisted automatic SCM model tuning workflow is proposed that dynamically interacts with the numerical model to optimize the reaction constants. The results show good zeta potential estimations from SCM reactions compared to experimental data. Meanwhile, the ANN optimized SCM model parameters improve the overall matching performance while significantly reducing human labor in the manual model adjustment.

^{2+}in this case) do not reversely affect the fluid transport process due to intermolecular interactions, such that the LBM fluid flow is purely driven by pressure differences and affected by the no-flow boundaries at the walls. We plan to include the intermolecular interaction effects in the future modeling approaches.

## Acknowledgments

This work was financially supported by the Department of Chemical and Petroleum Engineering, University of Kansas, and was partially supported by the Kansas Interdisciplinary Carbonates Consortium (KICC) as well as the ExxonMobil/GSA Student Geoscience Grant.

## Appendix

### Evaluation Metrics

*R*

^{2}) to evaluate the model validation results and the ANN prediction performance. MAE is defined as

*Y*

_{i}

^{obs}stands for the

*i*th observation, ${\overline{Y}}^{obs}$ is the mean value of the observations, and

*Y*

_{i}

^{pred}represents model or ANN predictions.

## Nomenclature

CFD | computational fluid mechanics |

LBM | lattice Boltzmann method |

EOR | enhanced oil recovery |

Ω | LBM collision operator |

τ | LBM relaxation time |

f_{k} | lattice distribution function |

f_{k}^{EQ} | lattice equilibrium distribution function |

ρ_{LB} | lattice fluid density |

P_{LB} | lattice pressure |

C_{s} | lattice speed of sound |

u_{LB} | lattice velocity |

ν_{LB} | lattice kinematic viscosity |

ω_{i} | weight coefficient for |

SCM | surface complexation model |

COBR | crude–oil–brine–rock |

ζ | zeta potential |

H | width of the flow channel |

u(y) | flow velocity profile along |

p | pressure |

μ | fluid viscosity |

nx | number of lattice nodes along the |

ny | number of lattice nodes along the |

c | species concentration |

c_{0} | constant species concentration at boundary |

c_{inf} | species concentration at the end of the assumed infinitely long channel |

D | diffusion coefficient |

rf | reaction rate factor |

k | rate constant |

γ | activity coefficient |

Re | Reynolds number |

Pe | Péclet number |

ξ | stoichiometric coefficient |

A | reacting surface area |

R | average reaction rate |

NN | neural network |

ANN | artificial neural network |

MLP | multi-layer perceptron |

SGD | stochastic gradient descent |

MAE | mean absolute error |

MSE | mean squared error |

R^{2} | coefficient of determination |

Y_{i}^{obs} | observation values |

Y_{i}^{pred} | prediction values |

K_{i} | reaction constant log |

FWS/SWS/LSW | formation water/seawater/low salinity water |

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The solid-fluid interaction is computed with the finite vol. method. The numerical method includes the migration of solid particles released due to dissoln. within the porous medium. The solid migration is realized by the cluster anal. and local movement. We validate this model by comparing against published dynamic micro-CT imaging expts. for dissoln. of a Ketton carbonate. To measure the local dissoln., the porosity profiles are compared with the published exptl. observations. The increases in permeability and porosity are investigated and a power law is derived to describe their relationship. Then, the significance of capturing the migration of solid particles released due to dissoln. on hydrol. properties of rocks is explored. The numerical approach is able to perform parallel simulation on large high-resoln. micro-CT images. We show the importance of simulation directly on micro-CT images without reducing the resoln. of rock micro-CT images. Further simulations are performed at Peclet regimes similar to sub-surface flow and the effect of flow rate on reactive transport is studied. This study illustrates the effect of inclusion of solid migration and the capability of simulation of reactive transport directly on high-resoln. images and helps understand the reactive transport at the pore scale.**28**Tian, Z.; Wang, J. Lattice Boltzmann Simulation of CO2 Reactive Transport in Network Fractured Media.*Water Resour. Res.*2017,*53*, 7366– 7381, DOI: 10.1002/2017wr021063Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsFWhurzF&md5=5188a687e295b3bcc3b6c6c0e5ad8614Lattice Boltzmann simulation of CO2 reactive transport in network fractured mediaTian, Zhiwei; Wang, JunyeWater Resources Research (2017), 53 (8), 7366-7381CODEN: WRERAQ; ISSN:0043-1397. (Wiley-Blackwell)Carbon dioxide (CO2) geol. sequestration plays an important role in mitigating CO2 emissions for climate change. Understanding interactions of the injected CO2 with network fractures and hydrocarbons is key for optimizing and controlling CO2 geol. sequestration and evaluating its risks to ground water. However, there is a well-known, difficult process in simulating the dynamic interaction of fracture-matrix, such as dynamic change of matrix porosity, unsatd. processes in rock matrix, and effect of rock mineral properties. In this paper, we develop an explicit model of the fracture-matrix interactions using multilayer bounce-back treatment as a first attempt to simulate CO2 reactive transport in network fractured media through coupling the Dardis's LBM porous model for a new interface treatment. Two kinds of typical fracture networks in porous media are simulated: straight cross network fractures and interleaving network fractures. The reaction rate and porosity distribution are illustrated and well-matched patterns are found. The species concn. distribution and evolution with time steps are also analyzed and compared with different transport properties. The results demonstrate the capability of this model to investigate the complex processes of CO2 geol. injection and reactive transport in network fractured media, such as dynamic change of matrix porosity.**29**Di Palma, P. R.; Huber, C.; Viotti, P. A New Lattice Boltzmann Model for Interface Reactions between Immiscible Fluids.*Adv. Water Resour.*2015,*82*, 139– 149, DOI: 10.1016/j.advwatres.2015.05.001Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXosVKksLY%253D&md5=ae4b020a394dc4f23167fc5d4ee71b4bA new lattice Boltzmann model for interface reactions between immiscible fluidsDi Palma, Paolo Roberto; Huber, Christian; Viotti, PaoloAdvances in Water Resources (2015), 82 (), 139-149CODEN: AWREDI; ISSN:0309-1708. (Elsevier Ltd.)In this paper, we describe a lattice Boltzmann model to simulate chem. reactions taking place at the interface between two immiscible fluids. The phase-field approach is used to identify the interface and its orientation, the concn. of reactant at the interface is then calcd. iteratively to impose the correct reactive flux condition. The main advantages of the model is that interfaces are considered part of the bulk dynamics with the corrective reactive flux introduced as a source/sink term in the collision step, and, as a consequence, the model's implementation and performance is independent of the interface geometry and orientation. Results obtained with the proposed model are compared to anal. soln. for three different benchmark tests (stationary flat boundary, moving flat boundary and dissolving droplet). We find an excellent agreement between anal. and numerical solns. in all cases. Finally, we present a simulation coupling the Shan Chen multiphase model and the interface reactive model to simulate the dissoln. of a collection of immiscible droplets with different sizes rising by buoyancy in a stagnant fluid.**30**Shan, X. Simulation of Rayleigh-Bénard convection using a lattice Boltzmann method.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*1997,*55*, 2780– 2788, DOI: 10.1103/physreve.55.2780Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXitVKitb4%253D&md5=7c13e835a4ec9796e12adf4010f5e707Simulation of Rayleigh-Benard convection using a lattice Boltzmann methodShan, XiaowenPhysical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics (1997), 55 (3-A), 2780-2788CODEN: PLEEE8; ISSN:1063-651X. (American Physical Society)Rayleigh-Benard convection is numerically simulated in two and three dimensions using a recently developed two-component lattice Boltzmann equation (LBE) method. The d. field of the second component, which evolves according to the advection-diffusion equation of a passive scalar, is used to simulate the temp. field. A body force proportional to the temp. is applied, and the system satisfies the Boussinesq equation except for a slight compressibility. A no-slip, isothermal boundary condition is imposed in the vertical direction, and periodic boundary conditions are used in horizontal directions. The crit. Rayleigh no. for the onset of the Rayleigh-Benard convection agrees with the theor. prediction. As the Rayleigh no. is increased higher, the steady two-dimensional convection rolls become unstable. The wavy instability and aperiodic motion obsd., as well as the Nusselt no. as a function of the Rayleigh no., are in good agreement with exptl. observations and theor. predictions. The LBE model is found to be efficient accurate, and numerically stable for the simulation of fluid flows with heat and mass transfer.**31**Huber, C.; Parmigiani, A.; Chopard, B.; Manga, M.; Bachmann, O. Lattice Boltzmann Model for Melting with Natural Convection.*Int. J. Heat Fluid Flow*2008,*29*, 1469– 1480, DOI: 10.1016/j.ijheatfluidflow.2008.05.002Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtFCksLvN&md5=e2a1c69e82786792d4ff6ab58b2996dcLattice Boltzmann model for melting with natural convectionHuber, Christian; Parmigiani, Andrea; Chopard, Bastien; Manga, Michael; Bachmann, OlivierInternational Journal of Heat and Fluid Flow (2008), 29 (5), 1469-1480CODEN: IJHFD2; ISSN:0142-727X. (Elsevier B.V.)We develop a lattice Boltzmann method to couple thermal convection and pure-substance melting. The transition from conduction-dominated heat transfer to fully-developed convection is analyzed and scaling laws and previous numerical results are reproduced by our numerical method. We also investigate the limit in which thermal inertia (high Stefan no.) cannot be neglected. We use our results to extend the scaling relations obtained at low Stefan no. and establish the correlation between the melting front propagation and the Stefan no. for fully-developed convection. We conclude by showing that the model presented here is particularly well-suited to study convection melting in geometrically complex media with many applications in geosciences.**32**Yoon, H.; Kang, Q.; Valocchi, A. J. 12. Lattice Boltzmann-Based Approaches for Pore-Scale Reactive Transport.*Rev. Mineral. Geochem.*2015,*80*, 393– 432, DOI: 10.1515/9781501502071-012Google ScholarThere is no corresponding record for this reference.**33**Luan, H. B.; Xu, H.; Chen, L.; Sun, D. L.; Tao, W. Q. Numerical Illustrations of the Coupling Between the Lattice Boltzmann Method and Finite-Type Macro-Numerical Methods.*Numer. Heat Transfer, Part B*2010,*57*, 147– 171, DOI: 10.1080/15421400903579929Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkslKqtLY%253D&md5=ad7bf3ecff067445e9d3c7f78a8be782Numerical illustrations of the coupling between the lattice Boltzmann method and finite-type macro-numerical methodsLuan, H. B.; Xu, H.; Chen, L.; Sun, D. L.; Tao, W. Q.Numerical Heat Transfer, Part B: Fundamentals (2010), 57 (2), 147-171CODEN: NHBFEE; ISSN:1040-7790. (Taylor & Francis, Inc.)An analytic expression called a reconstruction operator is proposed for the exchange from velocity of finite-type methods to the single-particle distribution function of the lattice Boltzmann method (LBM). The combined finite-vol. method and lattice Boltzmann method (called the CFVLBM) is adopted to solve three flow cases, backward-facing flow, flow around a circular cylinder, and lid-driven cavity flow. The results predicted by the CFVLBM agree with the available numerical solns. very well. It is shown that the vorticity contour distribution is a more appropriate parameter to ensure good smoothness and consistency at the coupling interface. At the same time, CPU time used by the CFVLBM(II), with more than one outer iteration before interface information exchange, is much less than that of the CFVLBM(I), where interface information exchanges are executed after each outer iteration.**34**Yu, D.; Mei, R.; Shyy, W. A Multi-Block Lattice Boltzmann Method for Viscous Fluid Flows.*Int. J. Numer. Methods Fluids*2002,*39*, 99– 120, DOI: 10.1002/fld.280Google ScholarThere is no corresponding record for this reference.**35**Luan, H.-B.; Xu, H.; Chen, L.; Sun, D.-L.; He, Y.-L.; Tao, W.-Q. Evaluation of the Coupling Scheme of FVM and LBM for Fluid Flows around Complex Geometries.*Int. J. Heat Mass Transfer*2011,*54*, 1975– 1985, DOI: 10.1016/j.ijheatmasstransfer.2011.01.004Google ScholarThere is no corresponding record for this reference.**36**Chen, L.; He, Y.-L.; Kang, Q.; Tao, W.-Q. Coupled Numerical Approach Combining Finite Volume and Lattice Boltzmann Methods for Multi-Scale Multi-Physicochemical Processes.*J. Comput. Phys.*2013,*255*, 83– 105, DOI: 10.1016/j.jcp.2013.07.034Google Scholar36https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFOgu7%252FJ&md5=ea0a7173f9b41b18f124bb2db4c0e3a4Coupled numerical approach combining finite volume and lattice Boltzmann methods for multi-scale multi-physicochemical processesChen, Li; He, Ya-Ling; Kang, Qinjun; Tao, Wen-QuanJournal of Computational Physics (2013), 255 (), 83-105CODEN: JCTPAH; ISSN:0021-9991. (Elsevier Inc.)A coupled (hybrid) simulation strategy spatially combining the finite vol. method (FVM) and the lattice Boltzmann method (LBM), called CFVLBM, is developed to simulate coupled multi-scale multi-physicochem. processes. In the CFVLBM, computational domain of multi-scale problems is divided into two sub-domains, i.e., an open, free fluid region and a region filled with porous materials. The FVM and LBM are used for these two regions, resp., with information exchanged at the interface between the two sub-domains. A general reconstruction operator (RO) is proposed to derive the distribution functions in the LBM from the corresponding macro scalar, the governing equation of which obeys the convection-diffusion equation. The CFVLBM and the RO are validated in several typical physicochem. problems and then are applied to simulate complex multi-scale coupled fluid flow, heat transfer, mass transport, and chem. reaction in a wall-coated micro reactor. The max. ratio of the grid size between the FVM and LBM regions is explored and discussed.**37**Sullivan, S. P.; Sani, F. M.; Johns, M. L.; Gladden, L. F. Simulation of Packed Bed Reactors Using Lattice Boltzmann Methods.*Chem. Eng. Sci.*2005,*60*, 3405– 3418, DOI: 10.1016/j.ces.2005.01.038Google Scholar37https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXjs1Kqtrc%253D&md5=c7f16251939208cb87ef397881b7a849Simulation of packed bed reactors using lattice Boltzmann methodsSullivan, S. P.; Sani, F. M.; Johns, M. L.; Gladden, L. F.Chemical Engineering Science (2005), 60 (12), 3405-3418CODEN: CESCAC; ISSN:0009-2509. (Elsevier Ltd.)Lattice Boltzmann (LB) methods are used to simulate hydrodynamics, reaction and subsequent mass transfer in a disordered packed bed of catalyst particles at sub-pore length-scales. In contrast to previous studies, a variety of modifications are introduced in the LB method enabling particle Peclet nos. ≤ 108, and hence realistic values of diffusivity, to be accessed. These include decoupling the hydrodynamics from mass transfer and the use of a rest fraction in the LB formulation of mass transfer. In addn. the mass transfer simulations are modified to permit spatially varying values of diffusivity, essential to differentiate between intra- and inter-particle diffusivity (Dintra and Dinter, resp.). The simulation method is applied to both a disordered and ordered 2D packing for a range of Peclet (15.6-1557.8) and Reynolds (0.16-1.56) nos., as well as various ratios of Dintra/Dinter (0-1), while simulating an esterification reaction catalyzed by an ion-exchange resin. The value of D intra is found to have limited effect, while reducing Peclet no. results in a considerable increase in overall conversion. The simulation method is then applied to a 3D lattice for which exptl. conversion data is available. This exptl. data is straddled by the simulation case of D intra = 0 and Dintra = Dinter, as expected.**38**Chen, L.; Kang, Q.; Robinson, B. A.; He, Y.-L.; Tao, W.-Q. Pore-Scale Modeling of Multiphase Reactive Transport with Phase Transitions and Dissolution-Precipitation Processes in Closed Systems.*Phys. Rev. E: Stat., Nonlinear, Soft Matter Phys.*2013,*87*, 43306, DOI: 10.1103/physreve.87.043306Google Scholar38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXovVahurY%253D&md5=04988a9a839ae47e240c053432561673Pore-scale modeling of multiphase reactive transport with phase transitions and dissolution-precipitation processes in closed systemsChen, Li; Kang, Qinjun; Robinson, Bruce A.; He, Ya-Ling; Tao, Wen-QuanPhysical Review E: Statistical, Nonlinear, and Soft Matter Physics (2013), 87 (4-B), 043306/1-043306/16CODEN: PRESCM; ISSN:1539-3755. (American Physical Society)A pore-scale model based on the Lattice Boltzmann (LB) method is developed for multiphase reactive transport with phase transitions and dissoln.-pptn. processes. The model combines the single-component multiphase Shan-Chen LB model, the mass transport LB model, and the dissoln.-pptn. model. Care is taken to handle information on computational nodes undergoing solid-liq. or liq.-vapor phase changes to guarantee mass and momentum conservation. A general LB concn. boundary condition is proposed that can handle various concn. boundaries including reactive and moving boundaries with complex geometries. The pore-scale model can capture coupled nonlinear multiple physicochem. processes including multiphase flow with phase sepns., mass transport, chem. reactions, dissoln.-pptn. processes, and dynamic evolution of the pore geometries. The model is validated using several multiphase flow and reactive transport problems and then used to study the thermal migration of a brine inclusion in a salt crystal. Multiphase reactive transport phenomena with phase transitions between liq.-vapor phases and dissoln.-pptn. processes of the salt in the closed inclusion are simulated and the effects of the initial inclusion size and temp. gradient on the thermal migration are investigated.**39**Kang, Q.; Zhang, D.; Lichtner, P. C.; Tsimpanogiannis, I. N. Lattice Boltzmann Model for Crystal Growth from Supersaturated Solution.*Geophys. Res. Lett.*2004,*31*, GL021107, DOI: 10.1029/2004gl021107Google ScholarThere is no corresponding record for this reference.**40**Chen, L.; Kang, Q.; Carey, B.; Tao, W. Q. Pore-Scale Study of Diffusion-Reaction Processes Involving Dissolution and Precipitation Using the Lattice Boltzmann Method.*Int. J. Heat Mass Transfer*2014,*75*, 483– 496, DOI: 10.1016/j.ijheatmasstransfer.2014.03.074Google ScholarThere is no corresponding record for this reference.**41**Kang, Q.; Chen, L.; Valocchi, A. J.; Viswanathan, H. S. Pore-Scale Study of Dissolution-Induced Changes in Permeability and Porosity of Porous Media.*J. Hydrol.*2014,*517*, 1049– 1055, DOI: 10.1016/j.jhydrol.2014.06.045Google ScholarThere is no corresponding record for this reference.**42**Chen, L.; Kang, Q.; Viswanathan, H. S.; Tao, W.-Q. Pore-scale study of dissolution-induced changes in hydrologic properties of rocks with binary minerals.*Water Resour. Res.*2014,*50*, 9343– 9365, DOI: 10.1002/2014wr015646Google ScholarThere is no corresponding record for this reference.**43**Parkhurst, B. D. L.*User’s Guide to PHREEQC ─ a Computer Program for Inverse Geochemical Calculations*; U.S. Geological Survey, 1995.Google ScholarThere is no corresponding record for this reference.**44**Charlton, S. R.; Parkhurst, D. L. Modules Based on the Geochemical Model PHREEQC for Use in Scripting and Programming Languages.*Comput. Geosci.*2011,*37*, 1653– 1663, DOI: 10.1016/j.cageo.2011.02.005Google Scholar44https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhtFGnsLrK&md5=b09d42ff22fe7e8dced6821edeb0d26aModules based on the geochemical model PHREEQC for use in scripting and programming languagesCharlton, Scott R.; Parkhurst, David L.Computers & Geosciences (2011), 37 (10), 1653-1663CODEN: CGEODT; ISSN:0098-3004. (Elsevier Ltd.)The geochem. model PHREEQC is capable of simulating a wide range of equil. reactions between water and minerals, ion exchangers, surface complexes, solid solns., and gases. It also has a general kinetic formulation that allows modeling of nonequil. mineral dissoln. and pptn., microbial reactions, decompn. of org. compds., and other kinetic reactions. To facilitate use of these reaction capabilities in scripting languages and other models, PHREEQC has been implemented in modules that easily interface with other software. A Microsoft COM (component object model) has been implemented, which allows PHREEQC to be used by any software that can interface with a COM server-for example, Excel, Visual Basic, Python, or MATLAB. PHREEQC has been converted to a C++ class, which can be included in programs written in C++. The class also has been compiled in libraries for Linux and Windows that allow PHREEQC to be called from C++, C, and Fortran. A limited set of methods implements the full reaction capabilities of PHREEQC for each module. Input methods use strings or files to define reaction calcns. in exactly the same formats used by PHREEQC. Output methods provide a table of user-selected model results, such as concns., activities, satn. indexes, and densities. The PHREEQC module can add geochem. reaction capabilities to surface-water, groundwater, and watershed transport models. It is possible to store and manipulate soln. compns. and reaction information for many cells within the module. In addn., the object-oriented nature of the PHREEQC modules simplifies implementation of parallel processing for reactive-transport models. The PHREEQC COM module may be used in scripting languages to fit parameters; to plot PHREEQC results for field, lab., or theor. investigations; or to develop new models that include simple or complex geochem. calcns.**45**Parkhurst, D. L.; Wissmeier, L. PhreeqcRM: A Reaction Module for Transport Simulators Based on the Geochemical Model PHREEQC.*Adv. Water Resour.*2015,*83*, 176– 189, DOI: 10.1016/j.advwatres.2015.06.001Google Scholar45https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVegtb7L&md5=06eb364cf39edb534432e811c5f5fb2aPhreeqcRM: A reaction module for transport simulators based on the geochemical model PHREEQCParkhurst, David L.; Wissmeier, LaurinAdvances in Water Resources (2015), 83 (), 176-189CODEN: AWREDI; ISSN:0309-1708. (Elsevier Ltd.)PhreeqcRM is a geochem. reaction module designed specifically to perform equil. and kinetic reaction calcns. for reactive transport simulators that use an operator-splitting approach. The basic function of the reaction module is to take component concns. from the model cells of the transport simulator, run geochem. reactions, and return updated component concns. to the transport simulator. If multicomponent diffusion is modeled (e.g., Nernst-Planck equation), then aq. species concns. can be used instead of component concns. The reaction capabilities are a complete implementation of the reaction capabilities of PHREEQC. In each cell, the reaction module maintains the compn. of all of the reactants, which may include minerals, exchangers, surface complexers, gas phases, solid solns., and user-defined kinetic reactants.PhreeqcRM assigns initial and boundary conditions for model cells based on std. PHREEQC input definitions (files or strings) of chem. compns. of solns. and reactants. Addnl. PhreeqcRM capabilities include methods to eliminate reaction calcns. for inactive parts of a model domain, transfer concns. and other model properties, and retrieve selected results. The module demonstrates good scalability for parallel processing by using multiprocessing with MPI (message passing interface) on distributed memory systems, and limited scalability using multithreading with OpenMP on shared memory systems. PhreeqcRM is written in C++, but interfaces allow methods to be called from C or Fortran. By using the PhreeqcRM reaction module, an existing multicomponent transport simulator can be extended to simulate a wide range of geochem. reactions. Results of the implementation of PhreeqcRM as the reaction engine for transport simulators PHAST and FEFLOW are shown by using an anal. soln. and the reactive transport benchmark of MoMaS.**46**Liu, S.; Zhang, C.; Ghahfarokhi, R. B. A Review of Lattice-Boltzmann Models Coupled with Geochemical Modeling Applied for Simulation of Advanced Waterflooding and Enhanced Oil Recovery Processes.*Energy Fuels*2021,*35*, 13535– 13549, DOI: 10.1021/acs.energyfuels.1c01347Google Scholar46https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXhvVegs73J&md5=f07540711341a48270d91ac342cac3d4A Review of Lattice-Boltzmann Models Coupled with Geochemical Modeling Applied for Simulation of Advanced Waterflooding and Enhanced Oil Recovery ProcessesLiu, Siyan; Zhang, Chi; Ghahfarokhi, Reza BaratiEnergy & Fuels (2021), 35 (17), 13535-13549CODEN: ENFUEM; ISSN:0887-0624. (American Chemical Society)A review. To maintain economic profit and improve the oil prodn. efficiency after the primary and secondary prodn. phase, advanced waterflooding techniques such as low salinity waterflooding in carbonate reservoirs have been investigated in numerical simulations, lab. expts., and field pilot tests. Multiple underlying mechanisms have been proposed based on these studies, and they are still under debate. Various numerical modeling approaches are introduced, but there exists a lack of a pore-scale comprehensive modeling scheme to fully understand the processes. Lattice-Boltzmann method (LBM) is a type of numerical fluid flow modeling technique that shows capabilities and flexibilities in modeling pore-scale fluid flow to integrate phys.-chem. processes within complex structures. The intrinsic feature of LBM makes it a promising framework for simulating advanced waterflooding due to its flexibility, accuracy, and parallel efficiency. LBM works either by itself for solving reactive transport problems or by coupling with a third-party reaction solver. This review mainly introduces the LBM fluid flow and reactive transport capabilities and the concept and modeling approaches to simulate advanced waterflooding techniques. Meanwhile, an evaluation of the coupled LBM models for enhanced oil recovery (EOR) simulations is discussed with future research challenges and directions concluded.**47**Kazemi Nia Korrani, A.; Jerauld, G. R.; Sepehrnoori, K. Coupled Geochemical-Based Modeling of Low Salinity Waterflooding.*SPE Improved Oil Recovery Symposium*, 2014, No. 2008; Vol. 1–23.Google ScholarThere is no corresponding record for this reference.**48**Patel, R.; Perko, J.; Jacques, D.; De Schutter, G.; Ye, G.; Van Breugel, K. Lattice Boltzmann Based Multicomponent Reactive Transport Model Coupled with Geochemical Solver for Scale Simulations.*Computational Methods for Coupled Problems in Science and Engineering*, 2013; pp 806– 817.Google ScholarThere is no corresponding record for this reference.**49**Patel, R. A.; Perko, J.; Jacques, D.; De Schutter, G.; Van Breugel, K.; Ye, G. A Versatile Pore-Scale Multicomponent Reactive Transport Approach Based on Lattice Boltzmann Method: Application to Portlandite Dissolution.*Phys. Chem. Earth*2014,*70–71*, 127– 137, DOI: 10.1016/j.pce.2014.03.001Google ScholarThere is no corresponding record for this reference.**50**Patel, R. A.; Perko, J.; Jacques, D.; De Schutter, G.; Ye, G.; Van Breugel, K. A Three-Dimensional Lattice Boltzmann Method Based Reactive Transport Model to Simulate Changes in Cement Paste Microstructure Due to Calcium Leaching.*Constr. Build. Mater.*2018,*166*, 158– 170, DOI: 10.1016/j.conbuildmat.2018.01.114Google Scholar50https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXjvFyhu7g%253D&md5=1a5b131cd868d07924d4c2b95204c3b4A three-dimensional lattice Boltzmann method based reactive transport model to simulate changes in cement paste microstructure due to calcium leachingPatel, Ravi A.; Perko, Janez; Jacques, Diederik; De Schutter, Geert; Ye, Guang; Van Breugel, KlaasConstruction and Building Materials (2018), 166 (), 158-170CODEN: CBUMEZ; ISSN:1879-0526. (Elsevier Ltd.)In this paper, a newly developed lattice Boltzmann method based reactive transport model to simulate changes in microstructure of ordinary Portland cement paste due to calcium leaching is presented. The model takes three-dimensional digitized cement paste microstructure as input and is capable to capture an evolution of microstructure due to leaching, accounting for the dissoln. of portlandite and corresponding increase in capillary porosity and the decalcification of C-S-H resulting in increase in gel porosity. The developed model has been applied to microstructures generated using two cement hydration models, CEMHYD3D and HYMSOTRUC, for three water-to-cement ratios. It was obsd. that the rate of leaching is directly proportional to ability of microstructure to transport calcium ions and higher fraction of percolated capillary pores result in higher rate of leaching. The model qual. reproduces exptl. obsd. changes in cement paste porosity and pore size distribution due to leaching. The quant. validation of model at this scale is not possible by comparison of leaching obtained expts. and simulations which can be attributed to several factors including the differences in the scales of expt. and modeling study presented in this paper.**51**Fazeli, H.; Patel, R.; Hellevang, H. Effect of Pore-Scale Mineral Spatial Heterogeneity on Chemically Induced Alterations of Fractured Rock: A Lattice Boltzmann Study.*Geofluids*2018,*2018*, 1– 28, DOI: 10.1155/2018/6046182Google ScholarThere is no corresponding record for this reference.**52**Fazeli, H.; Patel, R. A.; Ellis, B. R.; Hellevang, H. Three-Dimensional Pore-Scale Modeling of Fracture Evolution in Heterogeneous Carbonate Caprock Subjected to CO2-Enriched Brine.*Environ. Sci. Technol.*2019,*53*, 4630– 4639, DOI: 10.1021/acs.est.8b05653Google Scholar52https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXmsFCrt7c%253D&md5=0da9cda915a6a9a1d9560c3ea85e0f03Three-Dimensional Pore-Scale Modeling of Fracture Evolution in Heterogeneous Carbonate Caprock Subjected to CO2-Enriched BrineFazeli, Hossein; Patel, Ravi A.; Ellis, Brian R.; Hellevang, HelgeEnvironmental Science & Technology (2019), 53 (8), 4630-4639CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Fractures in caprocks overlying CO2 storage reservoirs can adversely affect the sealing capacity of the rocks. Interactions between acidified fluid and minerals with different reactivities along a fracture pathway can affect the chem. induced changes in hydrodynamic properties of fractures. To study porosity and permeability evolution of small-scale (millimeter scale) fractures, a three-dimensional pore-scale reactive transport model based on the lattice Boltzmann method has been developed. The model simulates the evolution of two different fractured carbonate-rich caprock samples subjected to a flow of CO2-rich brine. The results show that the existence of nonreactive minerals along the flow path can restrict the increase in permeability and the cubic law used to relate porosity and permeability in monomineral fractured systems is therefore not valid in multimineral systems. Moreover, the injection of CO2-acidified brine at high rates resulted in a more permeable fractured media in comparison to the case with lower injection rates. The overall rate of calcite dissoln. along the fracture decreased over time, confirming similar observations from previous continuum scale models. The presented 3D pore-scale model can be used to provide inputs for continuum scale models, such as improved porosity-permeability relationships for heterogeneous rocks, and also to investigate other reactive transport processes in the context of CO2 leakage in fractured seals.**53**Fazeli, H.; Masoudi, M.; Patel, R. A.; Aagaard, P.; Hellevang, H. Pore-Scale Modeling of Nucleation and Growth in Porous Media.*ACS Earth Space Chem.*2020,*4*, 249– 260, DOI: 10.1021/acsearthspacechem.9b00290Google Scholar53https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmsFCktQ%253D%253D&md5=717098fb63bf7cb682d66011a6b6c562Pore-Scale Modeling of Nucleation and Growth in Porous MediaFazeli, Hossein; Masoudi, Mohammad; Patel, Ravi A.; Aagaard, Per; Hellevang, HelgeACS Earth and Space Chemistry (2020), 4 (2), 249-260CODEN: AESCCQ; ISSN:2472-3452. (American Chemical Society)During the chem. interactions between fluid and minerals in different geol. processes, it is of high importance to predict where secondary ppts. form in the porous rocks as it helps correctly predict the hydrodynamic properties of the porous media. The reactive transport models developed for this purpose need to account for the nucleation process which is probabilistic by nature. To knowledge, the probabilistic nature of nucleation based on the classical nucleation theory was not accounted for previously in reactive transport models. The authors develop a new probabilistic nucleation model and incorporate it into a pore-scale reactive transport solver to simulate the mineral nucleation and growth in the porous media. Simulations are performed for different supersaturations, growth rates, and flow rates using a single-component mineral reaction. Simulations show that initial supersaturations strongly affect the pattern of secondary ppt. formation. Higher initial supersaturations lead to more uniformly dispersed nucleation on all the grains, while the lower initial supersaturations result in more isolated patterns. Decreasing the growth rate favors the formation of uniformly dispersed nuclei, whereas higher growth rates cause more isolated nucleation. Injection of fluid with a higher velocity gives rise to more pptn. Also, comparison of probabilistic and deterministic nucleation showed that the isolated nucleation patterns cannot be modeled using the deterministic approach. Permeability for the porous media is influenced by the pattern of secondary ppt. growth and generally, the permeability has a direct relation with the initial supersatn. and an inverse relation with the growth rate and the flow rate. Finally, the model was applied for simulation of calcite nucleation and growth on quartz grains. The calcite nucleation and growth exhibit similar behavior to those obsd. for single-species simulations.**54**Kazemi Nia Korrani, A.; Sepehrnoori, K.; Delshad, M. Coupling IPhreeqc with UTCHEM to Model Reactive Flow and Transport.*Comput. Geosci.*2015,*82*, 152– 169, DOI: 10.1016/j.cageo.2015.06.004Google Scholar54https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVOktbnL&md5=ce9c82dd34f3b4df4684063739df8066Coupling IPhreeqc with UTCHEM to model reactive flow and transportKazemi Nia Korrani, Aboulghasem; Sepehrnoori, Kamy; Delshad, MojdehComputers & Geosciences (2015), 82 (), 152-169CODEN: CGEODT; ISSN:0098-3004. (Elsevier Ltd.)A detailed step-by-step algorithm is presented through which we integrate IPhreeqc of the United Stated Geol. Survey (USGS) state-of-the-art geochem. package with UTCHEM for comprehensive reactive-transport modeling. UTCHEM is 3D multi-phase flow and transport research simulator developed in The University of Texas at Austin. On the other hand, IPhreeqc is the open-source modules of the USGS state-of-the-art geochem. package, PHREEQC. Through this coupling, we are able to simulate homogeneous and heterogeneous, irreversible, and ion-exchange and surface reactions under non-isothermal, non-isobaric and both local-equil. and kinetic conditions. All the data communications between UTCHEM and IPhreeqc is performed through the computer memory without writing/reading files. We further parallelize the geochem. module of UTCHEM-IPhreeqc in order to conduct field scale reservoir simulations. Our proposed coupling procedure can be implemented in any existing reservoir simulator for comprehensive reactive-transport modeling. One realistic case study is presented using UTCHEM-IPhreeqc.**55**Nardi, A.; Idiart, A.; Trinchero, P.; de Vries, L. M.; Molinero, J. Interface COMSOL-PHREEQC (ICP), an Efficient Numerical Framework for the Solution of Coupled Multiphysics and Geochemistry.*Comput. Geosci.*2014,*69*, 10– 21, DOI: 10.1016/j.cageo.2014.04.011Google Scholar55https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtVCjtrvI&md5=045348b6b7ed9426eeb1273874dfbf35Interface COMSOL-PHREEQC (iCP), an efficient numerical framework for the solution of coupled multiphysics and geochemistryNardi, Albert; Idiart, Andres; Trinchero, Paolo; de Vries, Luis Manuel; Molinero, JorgeComputers & Geosciences (2014), 69 (), 10-21CODEN: CGEODT; ISSN:0098-3004. (Elsevier Ltd.)This paper presents the development, verification and application of an efficient interface, denoted as iCP, which couples two standalone simulation programs: the general purpose Finite Element framework COMSOL Multiphysics and the geochem. simulator PHREEQC. The main goal of the interface is to maximize the synergies between the aforementioned codes, providing a numerical platform that can efficiently simulate a wide no. of multiphysics problems coupled with geochem. iCP is written in Java and uses the IPhreeqc C++ dynamic library and the COMSOL Java-API. Given the large computational requirements of the aforementioned coupled models, special emphasis has been placed on numerical robustness and efficiency. To this end, the geochem. reactions are solved in parallel by balancing the computational load over multiple threads. First, a benchmark exercise is used to test the reliability of iCP regarding flow and reactive transport. Then, a large scale thermo-hydro-chem. (THC) problem is solved to show the code capabilities. The results of the verification exercise are successfully compared with those obtained using PHREEQC and the application case demonstrates the scalability of a large scale model, at least up to 32 threads.**56**Muniruzzaman, M.; Rolle, M. Modeling Multicomponent Ionic Transport in Groundwater with IPhreeqc Coupling: Electrostatic Interactions and Geochemical Reactions in Homogeneous and Heterogeneous Domains.*Adv. Water Resour.*2016,*98*, 1– 15, DOI: 10.1016/j.advwatres.2016.10.013Google Scholar56https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xhslelsr%252FM&md5=22ba899e1ea15f8fa04941672dab91d4Modeling multicomponent ionic transport in groundwater with IPhreeqc coupling: Electrostatic interactions and geochemical reactions in homogeneous and heterogeneous domainsMuniruzzaman, Muhammad; Rolle, MassimoAdvances in Water Resources (2016), 98 (), 1-15CODEN: AWREDI; ISSN:0309-1708. (Elsevier Ltd.)The key role of small-scale processes like mol. diffusion and electrochem. migration has been increasingly recognized in multicomponent reactive transport in satd. porous media. In this study, we propose a two-dimensional multicomponent reactive transport model taking into account the electrostatic interactions during transport of charged ions in phys. and chem. heterogeneous porous media. The modeling approach is based on the local charge balance and on the description of compd.-specific and spatially variable diffusive/dispersive fluxes. The multicomponent ionic transport code is coupled with the geochem. code PHREEQC-3 by utilizing the IPhreeqc module, thus enabling to perform the geochem. calcns. included in the PHREEQC's reaction package. The multicomponent reactive transport code is benchmarked with different 1-D and 2-D transport problems. Successively, conservative and reactive transport examples are presented to demonstrate the capability of the proposed model to simulate transport of charged species in heterogeneous porous media with spatially variable phys. and chem. properties. The results reveal that the Coulombic cross-coupling between dispersive fluxes can significantly influence conservative as well as reactive transport of charged species both at the lab. and at the field scale.**57**Parkhurst, D. L.; Kipp, K. L.; Engesgaard, P.; Charlton, S. R.*PHAST─A Program for Simulating Ground-Water Flow, Solute Transport, and Multicomponent Geochemical Reactions*, U.S. Geological Survey Techniques and Methods 6-A8; U.S. Geological Survey, 2004.Google ScholarThere is no corresponding record for this reference.**58**Parkhurst, D.; Kipp, K.; Charlton, S.*PHAST Version 2 - A Program for Simulating Groundwater Flow, Solute Transport, and Multicomponent Geochemical Reactions*, Modeling Techniques, Book 6; U.S. Geological Survey, 2010.Google ScholarThere is no corresponding record for this reference.**59**Diersch, H. J. G.*FEFLOW: Finite Element Modeling of Flow, Mass and Heat Transport in Porous and Fractured Media*; Springer Science & Business Media, 2014.Google ScholarThere is no corresponding record for this reference.**60**Muniruzzaman, M.; Rolle, M. Multicomponent Ionic Transport Modeling in Physically and Electrostatically Heterogeneous Porous Media With PhreeqcRM Coupling for Geochemical Reactions.*Water Resour. Res.*2019,*55*, 11121– 11143, DOI: 10.1029/2019wr026373Google Scholar60https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhslaqurk%253D&md5=2c29e79882b7a2ff2d71d1f21d954078Multicomponent Ionic Transport Modeling in Physically and Electrostatically Heterogeneous Porous Media With PhreeqcRM Coupling for Geochemical ReactionsMuniruzzaman, Muhammad; Rolle, MassimoWater Resources Research (2019), 55 (12), 11121-11143CODEN: WRERAQ; ISSN:0043-1397. (Wiley-Blackwell)Low-permeability aquitards, such as clay layers and inclusions, are of utmost importance for contaminant transport in groundwater systems. Although most dissolved species, contaminants, and clay surfaces are charged, the role of electrostatic interactions in subsurface flow-through systems has not been extensively investigated. This study presents a two-dimensional multicomponent reactive transport investigation of diffusive/dispersive and electrostatic processes in homogeneous and heterogeneous clay systems. The proposed approach is based on multiple continua and is capable to accurately describe charge interactions during ionic transport in the free water, diffuse layer, and interlayer water of charged porous media. The diffuse layer compn. is simulated by considering a mean electrostatic potential following Donnan approach, whereas the interlayer compn. is calcd. by adopting the Gaines-Thomas convention. Diffusive/dispersive fluxes within each subcontinuum (free water, diffuse layer, and interlayer) are calcd. solving the Nernst-Planck equation while maintaining a net zero-charge flux. Furthermore, the multidimensional flow and transport model is coupled with the geochem. code PHREEQC, by utilizing the PhreeqcRM module, thus enabling great flexibility to access all PHREEQC's reaction capabilities. The code is benchmarked in 1-D systems against other software and previously published exptl. data. Successively, reactive transport simulations are performed in 2-D clayey-sandy flow-through domains with spatially variable phys. and electrostatic properties at both lab. and field scales. The results reveal that different properties of surface charge, diffuse layer, and Coulombic interactions impact the transport of charged species and lead to distinct spatial distribution of the ions in the different subcontinua and to significantly different breakthrough curves.**61**Fokina, D.; Muravleva, E.; Ovchinnikov, G.; Oseledets, I. Microstructure Synthesis Using Style-Based Generative Adversarial Networks.*Phys. Rev. E*2020,*101*, 43308, DOI: 10.1103/physreve.101.043308Google Scholar61https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtl2ms73N&md5=858978194b72c33fb84bc101338775e3Microstructure synthesis using style-based generative adversarial networksFokina, Daria; Muravleva, Ekaterina; Ovchinnikov, George; Oseledets, IvanPhysical Review E (2020), 101 (4), 043308CODEN: PREHBM; ISSN:2470-0053. (American Physical Society)A review. This work considers the usage of StyleGAN architecture for the task of microstructure synthesis. The task is the following: Given no. of samples of structure we try to generate similar samples at the same time preserving its properties. Since the considered architecture is not able to produce samples of sizes larger than the training images, we propose to use image quilting to merge fixed-sized samples. One of the key features of the considered architecture is that it uses multiple image resolns. We also investigate the necessity of such an approach.**62**Mosser, L.; Dubrule, O.; Blunt, M. J. Reconstruction of Three-Dimensional Porous Media Using Generative Adversarial Neural Networks.*Phys. Rev. E*2017,*96*, 43309, DOI: 10.1103/physreve.96.043309Google Scholar62https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXpslGmsQ%253D%253D&md5=7a7a3238a099fba7254c2406357dc5cfReconstruction of three-dimensional porous media using generative adversarial neural networksMosser, Lukas; Dubrule, Olivier; Blunt, Martin J.Physical Review E (2017), 96 (4), 043309/1-043309/17CODEN: PREHBM; ISSN:2470-0053. (American Physical Society)To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a no. of representative samples of the void-solid structure. While modern x-ray computer tomog. has made it possible to ext. three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often exptl. not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets.We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics.We successfully compare measures of pore morphol., such as the Euler characteristic, two-point statistics, and directional single-phase permeability of synthetic realizations with the calcd. properties of a bead pack, Berea sandstone, andKetton limestone. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.**63**Mosser, L.; Dubrule, O.; Blunt, M. J. Stochastic Reconstruction of an Oolitic Limestone by Generative Adversarial Networks.*Transp. Porous Media*2018,*125*, 81– 103, DOI: 10.1007/s11242-018-1039-9Google Scholar63https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXmslykt74%253D&md5=12ad2967d601f7cc1209ac565365e00eStochastic Reconstruction of an Oolitic Limestone by Generative Adversarial NetworksMosser, Lukas; Dubrule, Olivier; Blunt, Martin J.Transport in Porous Media (2018), 125 (1), 81-103CODEN: TPMEEI; ISSN:0169-3913. (Springer)Stochastic image reconstruction is a key part of modern digital rock physics and material anal. that aims to create representative samples of microstructures for upsampling, upscaling and uncertainty quantification. We present new results of a method of three-dimensional stochastic image reconstruction based on generative adversarial neural networks (GANs). GANs are a family of unsupervised learning methods that require no a priori inference of the probability distribution assocd. with the training data. Thanks to the use of two convolutional neural networks, the discriminator and the generator, in the training phase, and only the generator in the simulation phase, GANs allow the sampling of large and realistic volumetric images. We apply a GAN-based workflow of training and image generation to an oolitic Ketton limestone micro-CT unsegmented gray-level dataset. Minkowski functionals calcd. as a function of the segmentation threshold are compared between simulated and acquired images. Flow simulations are run on the segmented images, and effective permeability and velocity distributions of simulated flow are also compared. Results show that GANs allow a fast and accurate reconstruction of the evaluated image dataset. We discuss the performance of GANs in relation to other simulation techniques and stress the benefits resulting from the use of convolutional neural networks . We address a no. of challenges involved in GANs, in particular the representation of the probability distribution assocd. with the training data.**64**Feng, J.; He, X.; Teng, Q.; Ren, C.; Chen, H.; Li, Y. Reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Networks.*Phys. Rev. E*2019,*100*, 33308, DOI: 10.1103/physreve.100.033308Google Scholar64https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXkvFyksg%253D%253D&md5=198c2a1727c52550620f6818b95c72d9Reconstruction of porous media from extremely limited information using conditional generative adversarial networksFeng, Junxi; He, Xiaohai; Teng, Qizhi; Ren, Chao; Chen, Honggang; Li, YangPhysical Review E (2019), 100 (3), 033308CODEN: PREHBM; ISSN:2470-0053. (American Physical Society)Porous media are ubiquitous in both nature and engineering applications. Therefore, their modeling and understanding is of vital importance. In contrast to direct acquisition of three-dimensional (3D) images of this type of medium, obtaining its subregion (s) such as 2D images or several small areas can be feasible. Therefore, reconstructing whole images from limited information is a primary technique in these types of cases. Given data in practice cannot generally be detd. by users and may be incomplete or only partially informed, thus making existing reconstruction methods inaccurate or even ineffective. In particular, conditional generative adversarial network is utilized to learn the mapping between the input (a partial image) and output (a full image). To ensure the reconstruction accuracy, two simple but effective objective functions are proposed and then coupled with the other two functions to jointly constrain the training procedure. Our method is extensively tested on a variety of porous materials and validated by both visual inspection and quant. comparison. It is shown to be accurate, stable, and even fast (0.08 s for a 128×128 image reconstruction). The proposed approach can be readily extended by, for example, incorporating user-defined conditional data and an arbitrary no. of object functions into reconstruction, while being coupled with other reconstruction methods.**65**Liu, S.; Zhong, Z.; Takbiri-Borujeni, A.; Kazemi, M.; Fu, Q.; Yang, Y. A Case Study on Homogeneous and Heterogeneous Reservoir Porous Media Reconstruction by Using Generative Adversarial Networks.*Energy Procedia*2019,*158*, 6164, DOI: 10.1016/j.egypro.2019.01.493Google ScholarThere is no corresponding record for this reference.**66**Shams, R.; Masihi, M.; Boozarjomehry, R. B.; Blunt, M. J. Coupled Generative Adversarial and Auto-Encoder Neural Networks to Reconstruct Three-Dimensional Multi-Scale Porous Media.*J. Pet. Sci. Eng.*2020,*186*, 106794, DOI: 10.1016/j.petrol.2019.106794Google Scholar66https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXisVKrsbvE&md5=c0dee6d62fd2c986ad0eb87612878a66Coupled generative adversarial and auto-encoder neural networks to reconstruct three-dimensional multi-scale porous mediaShams, Reza; Masihi, Mohsen; Boozarjomehry, Ramin Bozorgmehry; Blunt, Martin J.Journal of Petroleum Science & Engineering (2020), 186 (), 106794CODEN: JPSEE6; ISSN:0920-4105. (Elsevier B.V.)In this study, coupled Generative Adversarial and Auto-Encoder neural networks have been used to reconstruct realizations of three-dimensional porous media. The gradient-descent-based optimization method is used for training and stabilizing the neural networks. The multi-scale reconstruction has been conducted for both sandstone and carbonate samples from an Iranian oilfield. The sandstone contains inter and intra-grain porosity. The generative adversarial network predicts the inter-grain pores and the auto-encoder provides the generative adversarial network result with intra-grain pores (micro-porosity). Different matching criteria, including porosity, permeability, auto-correlation function, and visual interpretation have been used to investigate the performance of the models. This methodol. provides researchers with a reliable method to reconstruct multi-scale realizations of porous media.**67**Varfolomeev, I.; Yakimchuk, I.; Safonov, I. An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples.*Computers*2019,*8*, 72, DOI: 10.3390/computers8040072Google ScholarThere is no corresponding record for this reference.**68**Niu, Y.; Mostaghimi, P.; Shabaninejad, M.; Swietojanski, P.; Armstrong, R. T. Digital Rock Segmentation for Petrophysical Analysis With Reduced User Bias Using Convolutional Neural Networks.*Water Resour. Res.*2020,*56*, e2019WR026597 DOI: 10.1029/2019wr026597Google ScholarThere is no corresponding record for this reference.**69**Karimpouli, S.; Tahmasebi, P. Image-Based Velocity Estimation of Rock Using Convolutional Neural Networks.*Neural Networks*2019,*111*, 89– 97, DOI: 10.1016/j.neunet.2018.12.006Google Scholar69https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3cjmtFWltg%253D%253D&md5=374b0da504ea8887ff9ce06f0bdb7c3aImage-based velocity estimation of rock using Convolutional Neural NetworksKarimpouli Sadegh; Tahmasebi PejmanNeural networks : the official journal of the International Neural Network Society (2019), 111 (), 89-97 ISSN:.Digital images of rock samples have been using extensively in Digital Rock Physics (DRP) to evaluate physical parameters of rock such as permeability, P- and S-wave velocities and formation factor. The parameters are numerically computed by simulation of the corresponding physical processes through segmented image of rock, which provide a direct and accurate evaluation of rock properties. However, recent advances in machine learning and Convolutional Neural Networks (CNN) allow using images as input. Such networks, however, require a considerable number of images as input. In this paper, CNNs are used to estimate the P- and S-wave velocities from images of rock medium. To deal with lack of input data, a hybrid pattern- and pixel-based simulation (HYPPS) is used as an efficient data augmentation method to increase the training data set. For each input image, 10 stochastic realizations are produced. Compare to the case wherein the stochastic models are not used, the new results from the enhanced network indicate a sharp improvement in the estimations such that R(2) is increased to 0.94. Furthermore, the newly developed CNN network, unlike the one with the small data set (R(2)=0.75), manifests no over/underestimation. The estimated properties, in comparison with the computational results, indicate that CNNs perform outstandingly in predicting the physical parameters of rock without conducting any time-demanding forward modeling if enough input data are provided.**70**Wang, Y. D.; Shabaninejad, M.; Armstrong, R. T.; Mostaghimi, P. Deep Neural Networks for Improving Physical Accuracy of 2D and 3D Multi-Mineral Segmentation of Rock Micro-CT Images.*Appl. Soft Comput.*2021,*104*, 107185, DOI: 10.1016/j.asoc.2021.107185Google ScholarThere is no corresponding record for this reference.**71**Kamrava, S.; Tahmasebi, P.; Sahimi, M. Enhancing Images of Shale Formations by a Hybrid Stochastic and Deep Learning Algorithm.*Neural Networks*2019,*118*, 310– 320, DOI: 10.1016/j.neunet.2019.07.009Google Scholar71https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3MzpsVensQ%253D%253D&md5=dc11a632302781074ebbd56c10076c7fEnhancing images of shale formations by a hybrid stochastic and deep learning algorithmKamrava Serveh; Tahmasebi Pejman; Sahimi MuhammadNeural networks : the official journal of the International Neural Network Society (2019), 118 (), 310-320 ISSN:.Accounting for the morphology of shale formations, which represent highly heterogeneous porous media, is a difficult problem. Although two- or three-dimensional images of such formations may be obtained and analyzed, they either do not capture the nanoscale features of the porous media, or they are too small to be an accurate representative of the media, or both. Increasing the resolution of such images is also costly. While high-resolution images may be used to train a deep-learning network in order to increase the quality of low-resolution images, an important obstacle is the lack of a large number of images for the training, as the accuracy of the network's predictions depends on the extent of the training data. Generating a large number of high-resolution images by experimental means is, however, very time consuming and costly, hence limiting the application of deep-learning algorithms to such an important class of problems. To address the issue we propose a novel hybrid algorithm by which a stochastic reconstruction method is used to generate a large number of plausible images of a shale formation, using very few input images at very low cost, and then train a deep-learning convolutional network by the stochastic realizations. We refer to the method as hybrid stochastic deep-learning (HSDL) algorithm. The results indicate promising improvement in the quality of the images, the accuracy of which is confirmed by visual, as well as quantitative comparison between several of their statistical properties. The results are also compared with those obtained by the regular deep learning algorithm without using an enriched and large dataset for training, as well as with those generated by bicubic interpolation.**72**Kamrava, S.; Tahmasebi, P.; Sahimi, M. Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning.*Transp. Porous Media*2020,*131*, 427– 448, DOI: 10.1007/s11242-019-01352-5Google ScholarThere is no corresponding record for this reference.**73**Tembely, M.; AlSumaiti, A.*Deep Learning for a Fast and Accurate Prediction of Complex Carbonate Rock Permeability From 3D Micro-CT Images*; Abu Dhabi International Petroleum Exhibition and Conference, 2019.Google ScholarThere is no corresponding record for this reference.**74**Wu, J.; Yin, X.; Xiao, H. Seeing Permeability from Images: Fast Prediction with Convolutional Neural Networks.*Sci. Bull.*2018,*63*, 1215– 1222, DOI: 10.1016/j.scib.2018.08.006Google Scholar74https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB28fgtVCgsw%253D%253D&md5=045f16bb8df4840e385966fecd384dbfSeeing permeability from images: fast prediction with convolutional neural networksWu Jinlong; Yin Xiaolong; Xiao HengScience bulletin (2018), 63 (18), 1215-1222 ISSN:.Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples, (2) computation of permeability via fluid dynamics simulations, (3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially for those with dilated pores. Owning to such heterogeneity, the permeability cannot be estimated using the conventional Kozeny-Carman approach. Computational time was reduced by several orders of magnitude compared to fluid dynamic simulations. We found that, by including physical parameters that are known to affect permeability into the neural network, the physics-informed CNN generated better results than regular CNN. However, improvements vary with implemented heterogeneity.**75**Liu, S.; Zolfaghari, A.; Sattarin, S.; Dahaghi, A. K.; Negahban, S. Application of Neural Networks in Multiphase Flow through Porous Media: Predicting Capillary Pressure and Relative Permeability Curves.*J. Pet. Sci. Eng.*2019,*180*, 445– 455, DOI: 10.1016/j.petrol.2019.05.041Google Scholar75https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXltlOlu7s%253D&md5=d6fb170eb442a650ed6686dd33a3860aApplication of neural networks in multiphase flow through porous media: Predicting capillary pressure and relative permeability curvesLiu, Siyan; Zolfaghari, Arsalan; Sattarin, Shariar; Dahaghi, Amirmasoud Kalantari; Negahban, ShahinJournal of Petroleum Science & Engineering (2019), 180 (), 445-455CODEN: JPSEE6; ISSN:0920-4105. (Elsevier B.V.)Artificial Neural Networks (ANN) are trained to simulate two-phase capillary pressure and relative permeability data in bundles of capillary tubes with non-uniform arbitrary wettability conditions and cross-sectional shapes of different irregular convex polygons. All polygons with variable no. of corners are randomly generated for a given range of inscribed radii, shape, and elongation factors. To generate the data for the training of ANNs, the minimization of Helmholtz free energy and Mayer-Stowe-Princen (MS-P) method are combined to find thermodynamically consistent threshold capillary pressures for two-phase flow. These capillary pressures are then used to det. the sequence of displacements in different capillary tubes. We calc. saturations and phase conductance at each quasi steady-state condition where no more displacements can be done for a given capillary pressure. The generated two-phase capillary pressure and relative permeability curves are then used for the training of ANNs. We test different designs of ANNs to find the optimal workflow for the training and predicting of petrophys. properties related to multiphase flow. In this work, we present the results of two different neural network structures. In the first structure, we use ANN to predict threshold capillary pressures of different capillary tubes during a drainage process (i.e., oil-to-water displacements). In the second structure, we predict capillary pressure and relative permeability curves for an arbitrary bundle of capillary tubes. The first structure of ANNs simulates a fixed property for a given capillary tube, whereas the second structure simulates time-series data format (i.e., for a given bundle of capillary tubes calcd. properties vary with satn.). To do so, we have generated multi-phase flow properties for two large datasets consisting of 40,000 and 60,000 capillary tubes each. High-quality training datasets are crit. in the training of high-fidelity ANN models. These models can then learn the impact of a wide variety of pore geometries (i.e., shape factors and elongations). Addnl., feature selection and preprocessing of the input data could significantly impact ANN's predictions. The multi-layer perceptron (MLP) neural network with three hidden layers with four outputs is adequate for predicting capillary pressure and relative permeability curves during drainage. This model is approx. an order of magnitude faster than conventional direct calcns. using a desktop computer with four cores CPU. Such improvement in the speed of calcns. becomes significant when dealing with larger models, more dimensions, and/or introducing pore connectivity in 3D.**76**Liu, S.; Barati, R.; Zhang, C. Fast Estimation of Permeability in Sandstones by 3D Convolutional Neural Networks.*SEG International Exposition and Annual Meeting*, September 15, 2019; p D033S046R002.Google ScholarThere is no corresponding record for this reference.**77**Wu, H.; Fang, W.-Z.; Kang, Q.; Tao, W.-Q.; Qiao, R. Predicting Effective Diffusivity of Porous Media from Images by Deep Learning.*Sci. Rep.*2019,*9*, 20387, DOI: 10.1038/s41598-019-56309-xGoogle Scholar77https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmtFCjtQ%253D%253D&md5=89fb59c778f726be938c908a869c3326Predicting Effective Diffusivity of Porous Media from Images by Deep LearningWu, Haiyi; Fang, Wen-Zhen; Kang, Qinjun; Tao, Wen-Quan; Qiao, RuiScientific Reports (2019), 9 (1), 20387CODEN: SRCEC3; ISSN:2045-2322. (Nature Research)We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topol., large variation of porosity (0.28-0.98), and effective diffusivity spanning more than one order of magnitude (0.1 .ltorsim. De < 1), e.g., >95% of predicted De have truncated relative error of <10% when the true De is larger than 0.2. The CNN model provides better prediction than the empirical Bruggeman equation, esp. for porous structure with small diffusivity. The relative error of CNN predictions, however, is rather high for structures with De < 0.1. To address this issue, the porosity of porous structures is encoded directly into the neural network but the performance is enhanced marginally. Further improvement, i.e., 70% of the CNN predictions for structures with true De < 0.1 have relative error <30%, is achieved by removing trapped regions and dead-end pathways using a simple algorithm. These results suggest that deep learning augmented by field knowledge can be a powerful technique for predicting the transport properties of porous media. Directions for future research of machine learning in porous media are discussed based on detailed anal. of the performance of CNN models in the present work.**78**Wei, H.; Zhao, S.; Rong, Q.; Bao, H. Predicting the Effective Thermal Conductivities of Composite Materials and Porous Media by Machine Learning Methods.*Int. J. Heat Mass Transfer*2018,*127*, 908– 916, DOI: 10.1016/j.ijheatmasstransfer.2018.08.082Google ScholarThere is no corresponding record for this reference.**79**Zhang, Z.; Hong, Y.; Hou, B.; Zhang, Z.; Negahban, M.; Zhang, J. Accelerated Discoveries of Mechanical Properties of Graphene Using Machine Learning and High-Throughput Computation.*Carbon*2019,*148*, 115– 123, DOI: 10.1016/j.carbon.2019.03.046Google Scholar79https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXmtF2mt7s%253D&md5=450bc2084d9ed3846b7134f54419c824Accelerated discoveries of mechanical properties of graphene using machine learning and high-throughput computationZhang, Zesheng; Hong, Yang; Hou, Bo; Zhang, Zhongtao; Negahban, Mehrdad; Zhang, JingchaoCarbon (2019), 148 (), 115-123CODEN: CRBNAH; ISSN:0008-6223. (Elsevier Ltd.)Machine learning (ML) has been vastly used in various fields, but its application in engineering science remains in infancy. In this work, for the first time, different machine learning algorithms and artificial neural network (ANN) structures are used to predict the mech. properties of single-layer graphene under various impact factors of system temp., strain rate, vacancy defect and chirality. The predictions include fracture strain, fracture strength and Young's modulus. High throughput computation (HTC) combined with classical mol. dynamics (MD) simulation is used to generate the training dataset for the ML models. It was discovered that both temp. and vacancy defect have neg. effects on the predicted properties while strain rate has pos. correlations with the prediction results. The stochastic gradient descent (SGD) method could not properly capture the effects of the different impact factors on the mech. properties of graphene, while k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT) and ANN provided desirable prediction results. Discoveries in this work provide new perspectives on the study of mech. properties using state-of-the-art computational methods.**80**Santos, J. E.; Xu, D.; Jo, H.; Landry, C. J.; Prodanović, M.; Pyrcz, M. J. PoreFlow-Net: A 3D Convolutional Neural Network to Predict Fluid Flow through Porous Media.*Adv. Water Resour.*2020,*138*, 103539, DOI: 10.1016/j.advwatres.2020.103539Google ScholarThere is no corresponding record for this reference.**81**Liu, M.; Kwon, B.; Kang, P. K. Machine Learning to Predict Effective Reaction Rates in 3D Porous Media from Pore Structural Features.*Sci. Rep.*2022,*12*, 5486, DOI: 10.1038/s41598-022-09495-0Google Scholar81https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xosl2guro%253D&md5=0768add057d41b6b581a597adee73163Machine learning to predict effective reaction rates in 3D porous media from pore structural featuresLiu, Min; Kwon, Beomjin; Kang, Peter K.Scientific Reports (2022), 12 (1), 5486CODEN: SRCEC3; ISSN:2045-2322. (Nature Portfolio)Abstr.: Large discrepancies between well-mixed reaction rates and effective reactions rates estd. under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning (ML). We first perform pore-scale reactive transport simulations with fluid-solid reactions in hundreds of porous media and calc. effective reaction rates from pore-scale concn. fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in detg. effective reaction rates. Based on the importance information, we train artificial neural networks with varying no. of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are sp. surface, pore sphericity, and coordination no. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.**82**Tahmasebi, P.; Kamrava, S.; Bai, T.; Sahimi, M. Machine Learning in Geo- and Environmental Sciences: From Small to Large Scale.*Adv. Water Resour.*2020,*142*, 103619, DOI: 10.1016/j.advwatres.2020.103619Google ScholarThere is no corresponding record for this reference.**83**Wang, Y. D.; Blunt, M. J.; Armstrong, R. T.; Mostaghimi, P. Deep Learning in Pore Scale Imaging and Modeling.*Earth Sci. Rev.*2021,*215*, 103555, DOI: 10.1016/j.earscirev.2021.103555Google ScholarThere is no corresponding record for this reference.**84**Latt, J.; Malaspinas, O.; Kontaxakis, D.; Parmigiani, A.; Lagrava, D.; Brogi, F.; Belgacem, M. B.; Thorimbert, Y.; Leclaire, S.; Li, S.; Marson, F.; Lemus, J.; Kotsalos, C.; Conradin, R.; Coreixas, C.; Petkantchin, R.; Raynaud, F.; Beny, J.; Chopard, B. Palabos: Parallel Lattice Boltzmann Solver.*Comput. Math. Appl.*2021,*81*, 334– 350, DOI: 10.1016/j.camwa.2020.03.022Google ScholarThere is no corresponding record for this reference.**85**Parkhurst, D. L.*User’s Guide to PHREEQC, a Computer Program for Speciation, Reaction-Path, Advective-Transport, and Inverse Geochemical Calculations*; U.S. Geological Survey, 1995.Google ScholarThere is no corresponding record for this reference.**86**Bhatnagar, P. L.; Gross, E. P.; Krook, M. A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems.*Phys. Rev.*1954,*94*, 511– 525, DOI: 10.1103/physrev.94.511Google Scholar86https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaG2cXksVKhtg%253D%253D&md5=6f3a8c8f4fa1c6b7ca7af1dcc779a2c4A model for collision processes in gases. I. Small-amplitude processes in charged and neutral one-component systemsBhatnagar, P. L.; Gross, E. P.; Krook, M.Physical Review (1954), 94 (), 511-25CODEN: PHRVAO; ISSN:0031-899X.A kinetic-theory approach to collision processes in ionized and neutral gases is presented. This approach is adequate for the unified treatment of the dynamic properties of gases over a continuous range of pressures from the Knudsen limit to the high-pressure limit where the aerodynamic equations are valid. It is also possible to satisfy the correct microscopic boundary conditions. The method consists in altering the collision terms in the Boltzmann equation. The modified collision terms are constructed so that each collision conserves particle no., momentum, and energy; other characteristics such as persistence of velocities and angular dependence may be included. The technique is illustrated for a simple model involving the assumption of a collision time independent of velocity; this model is applied to the study of small amplitude oscillations of one-component ionized and neutral gases. The initial value problem for unbounded space is solved by performing a Fourier transformation on the space variables and a Laplace transformation on the time variable. For uncharged gases there results the correct adiabatic limiting law for sound-wave propagation at high pressures and, in addn., a theory of absorption and dispersion of sound for arbitrary pressures is obtained. For ionized gases the difference in the nature of the organization in the low-pressure plasma oscillations and in high-pressure sound-type oscillations is studied. Two important cases are distinguished. If the wave lengths of the oscillations are long compared to either the Debye length or the mean free path, a small change in frequency is obtained as the collision frequency varies from zero to infinity. The accompanying absorption is small; it reaches its max. value when the collision frequency equals the plasma frequency. The 2nd case refers to waves shorter than both the Debye length and the mean free path; these waves are characterized by a very heavy absorption.**87**Martys, N. N.; Douglas, J. J. Critical Properties and Phase Separation in Lattice Boltzmann Fluid Mixtures.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*2001,*63*, 031205, DOI: 10.1103/physreve.63.031205Google ScholarThere is no corresponding record for this reference.**88**Qian, Y. H.; D’Humières, D.; Lallemand, P. Lattice Bgk Models for Navier-Stokes Equation.*EPL*1992,*17*, 479– 484, DOI: 10.1209/0295-5075/17/6/001Google ScholarThere is no corresponding record for this reference.**89**Martys, N. S.; Chen, H. Simulation of Multicomponent Fluids in Complex Three-Dimensional Geometries by the Lattice Boltzmann Method.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*1996,*53*, 743– 750, DOI: 10.1103/physreve.53.743Google Scholar89https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28Xnsl2itw%253D%253D&md5=d06bd1d7428e69961f672bea767dc73fSimulation of multicomponent fluids in complex three-dimensional geometries by the lattice Boltzmann methodMartys, Nicos S.; Chen, HudongPhysical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics (1996), 53 (1-B), 743-750CODEN: PLEEE8; ISSN:1063-651X. (American Physical Society)We describe an implementation of a the recently proposed lattice Boltzmann based model of Shan and Chen [Phys. Rev. E 47, 1815 (1993); 49, 2941 (1994)] sto simulate multicomponent flow in complex three-dimensional geometries such as porous media. The above method allows for the direct incorporation of fluid-fluid and fluid-solid interactiosn as well as an applied external force. As a test of this method, we obtained Poiseuille flow for the case of a single fluid driven by a const. body force and obtained results consistent with Laplace's law for the case of two immiscible fluids. The displacement of one fluid by another in a porous medium was then modeled. The relative permeability for different wetting fluid saturatios of a microtomog.-generated image of sandstone was calcd. and compared favorably with expt. In addn., we show that a first-order phase transition, in three dimensions, may be obtained by this lattice Boltzman method, demonstrating the potential for modeling phase transitions and multiphase flow in porous media.**90**Timm, K.; Halim, K.; Alexandr, K.; Orest, S.; Goncalo, S.; Erlend, M. V.*The Lattice Boltzmann Method Principles and Practice*; Springer, 2017.Google ScholarThere is no corresponding record for this reference.**91**Huber, C.; Parmigiani, A.; Chopard, B.; Manga, M.; Bachmann, O. Lattice Boltzmann Model for Melting with Natural Convection.*Int. J. Heat Fluid Flow*2008,*29*, 1469– 1480, DOI: 10.1016/j.ijheatfluidflow.2008.05.002Google Scholar91https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtFCksLvN&md5=e2a1c69e82786792d4ff6ab58b2996dcLattice Boltzmann model for melting with natural convectionHuber, Christian; Parmigiani, Andrea; Chopard, Bastien; Manga, Michael; Bachmann, OlivierInternational Journal of Heat and Fluid Flow (2008), 29 (5), 1469-1480CODEN: IJHFD2; ISSN:0142-727X. (Elsevier B.V.)We develop a lattice Boltzmann method to couple thermal convection and pure-substance melting. The transition from conduction-dominated heat transfer to fully-developed convection is analyzed and scaling laws and previous numerical results are reproduced by our numerical method. We also investigate the limit in which thermal inertia (high Stefan no.) cannot be neglected. We use our results to extend the scaling relations obtained at low Stefan no. and establish the correlation between the melting front propagation and the Stefan no. for fully-developed convection. We conclude by showing that the model presented here is particularly well-suited to study convection melting in geometrically complex media with many applications in geosciences.**92**Parmigiani, A. Lattice Boltzmann Calculations of Reactive Multiphase Flows in Porous Media, Thesis, University of Geneva, 2011; Vol. 129.Google ScholarThere is no corresponding record for this reference.**93**Fazeli, H.; Patel, R. A.; Ellis, B. R.; Hellevang, H. Three-Dimensional Pore-Scale Modeling of Fracture Evolution in Heterogeneous Carbonate Caprock Subjected to CO2-Enriched Brine.*Environ. Sci. Technol.*2019,*53*, 4630– 4639, DOI: 10.1021/acs.est.8b05653Google Scholar93https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXmsFCrt7c%253D&md5=0da9cda915a6a9a1d9560c3ea85e0f03Three-Dimensional Pore-Scale Modeling of Fracture Evolution in Heterogeneous Carbonate Caprock Subjected to CO2-Enriched BrineFazeli, Hossein; Patel, Ravi A.; Ellis, Brian R.; Hellevang, HelgeEnvironmental Science & Technology (2019), 53 (8), 4630-4639CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Fractures in caprocks overlying CO2 storage reservoirs can adversely affect the sealing capacity of the rocks. Interactions between acidified fluid and minerals with different reactivities along a fracture pathway can affect the chem. induced changes in hydrodynamic properties of fractures. To study porosity and permeability evolution of small-scale (millimeter scale) fractures, a three-dimensional pore-scale reactive transport model based on the lattice Boltzmann method has been developed. The model simulates the evolution of two different fractured carbonate-rich caprock samples subjected to a flow of CO2-rich brine. The results show that the existence of nonreactive minerals along the flow path can restrict the increase in permeability and the cubic law used to relate porosity and permeability in monomineral fractured systems is therefore not valid in multimineral systems. Moreover, the injection of CO2-acidified brine at high rates resulted in a more permeable fractured media in comparison to the case with lower injection rates. The overall rate of calcite dissoln. along the fracture decreased over time, confirming similar observations from previous continuum scale models. The presented 3D pore-scale model can be used to provide inputs for continuum scale models, such as improved porosity-permeability relationships for heterogeneous rocks, and also to investigate other reactive transport processes in the context of CO2 leakage in fractured seals.**94**Fazeli, H.; Masoudi, M.; Patel, R. A.; Aagaard, P.; Hellevang, H. Pore-Scale Modeling of Nucleation and Growth in Porous Media.*ACS Earth Space Chem.*2020,*4*, 249– 260, DOI: 10.1021/acsearthspacechem.9b00290Google Scholar94https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmsFCktQ%253D%253D&md5=717098fb63bf7cb682d66011a6b6c562Pore-Scale Modeling of Nucleation and Growth in Porous MediaFazeli, Hossein; Masoudi, Mohammad; Patel, Ravi A.; Aagaard, Per; Hellevang, HelgeACS Earth and Space Chemistry (2020), 4 (2), 249-260CODEN: AESCCQ; ISSN:2472-3452. (American Chemical Society)During the chem. interactions between fluid and minerals in different geol. processes, it is of high importance to predict where secondary ppts. form in the porous rocks as it helps correctly predict the hydrodynamic properties of the porous media. The reactive transport models developed for this purpose need to account for the nucleation process which is probabilistic by nature. To knowledge, the probabilistic nature of nucleation based on the classical nucleation theory was not accounted for previously in reactive transport models. The authors develop a new probabilistic nucleation model and incorporate it into a pore-scale reactive transport solver to simulate the mineral nucleation and growth in the porous media. Simulations are performed for different supersaturations, growth rates, and flow rates using a single-component mineral reaction. Simulations show that initial supersaturations strongly affect the pattern of secondary ppt. formation. Higher initial supersaturations lead to more uniformly dispersed nucleation on all the grains, while the lower initial supersaturations result in more isolated patterns. Decreasing the growth rate favors the formation of uniformly dispersed nuclei, whereas higher growth rates cause more isolated nucleation. Injection of fluid with a higher velocity gives rise to more pptn. Also, comparison of probabilistic and deterministic nucleation showed that the isolated nucleation patterns cannot be modeled using the deterministic approach. Permeability for the porous media is influenced by the pattern of secondary ppt. growth and generally, the permeability has a direct relation with the initial supersatn. and an inverse relation with the growth rate and the flow rate. Finally, the model was applied for simulation of calcite nucleation and growth on quartz grains. The calcite nucleation and growth exhibit similar behavior to those obsd. for single-species simulations.**95**Patel, R.; Perko, J.; Jacques, D.; de Schutter, G.; Ye, G.; van Breugel, K. Lattice Boltzmann Based Multicomponent Reactive Transport Model Coupled with Geochemical Solver for Scale Simulations.*Computational Methods for Coupled Problems in Science and Engineering*, 2013; pp 806– 817.Google ScholarThere is no corresponding record for this reference.**96**Patel, R. A.; Perko, J.; Jacques, D.; de Schutter, G.; van Breugel, K.; Ye, G. A Versatile Pore-Scale Multicomponent Reactive Transport Approach Based on Lattice Boltzmann Method: Application to Portlandite Dissolution.*Phys. Chem. Earth*2014,*70–71*, 127– 137, DOI: 10.1016/j.pce.2014.03.001Google ScholarThere is no corresponding record for this reference.**97**Yoon, H.; Kang, Q.; Valocchi, A. J. 12. Lattice Boltzmann-Based Approaches for Pore-Scale Reactive Transport.*Rev. Mineral. Geochem.*2015,*80*, 393– 432, DOI: 10.1515/9781501502071-012Google ScholarThere is no corresponding record for this reference.**98**Latt, J.; Malaspinas, O.; Kontaxakis, D.; Parmigiani, A.; Lagrava, D.; Brogi, F.; Belgacem, M. B.; Thorimbert, Y.; Leclaire, S.; Li, S.; Marson, F.; Lemus, J.; Kotsalos, C.; Conradin, R.; Coreixas, C.; Petkantchin, R.; Raynaud, F.; Beny, J.; Chopard, B. Palabos: Parallel Lattice Boltzmann Solver.*Computers & Mathematics with Applications*, 2020.Google ScholarThere is no corresponding record for this reference.**99**Tan, J.; Sinno, T. R.; Diamond, S. L. A parallel fluid-solid coupling model using LAMMPS and Palabos based on the immersed boundary method.*J. Comput. Sci.*2018,*25*, 89– 100, DOI: 10.1016/j.jocs.2018.02.006Google Scholar99https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3c3ptlOktw%253D%253D&md5=8161f6ee17165f61ed137ec8cc917ffcA parallel fluid-solid coupling model using LAMMPS and Palabos based on the immersed boundary methodTan Jifu; Sinno Talid; Diamond Scott LJournal of computational science (2018), 25 (), 89-100 ISSN:1877-7503.The study of viscous fluid flow coupled with rigid or deformable solids has many applications in biological and engineering problems, e.g., blood cell transport, drug delivery, and particulate flow. We developed a partitioned approach to solve this coupled Multiphysics problem. The fluid motion was solved by Palabos (Parallel Lattice Boltzmann Solver), while the solid displacement and deformation was simulated by LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator). The coupling was achieved through the immersed boundary method (IBM). The code modeled both rigid and deformable solids exposed to flow. The code was validated with the Jeffery orbits of an ellipsoid particle in shear flow, red blood cell stretching test, and effective blood viscosity flowing in tubes. It demonstrated essentially linear scaling from 512 to 8192 cores for both strong and weak scaling cases. The computing time for the coupling increased with the solid fraction. An example of the fluid-solid coupling was given for flexible filaments (drug carriers) transport in a flowing blood cell suspensions, highlighting the advantages and capabilities of the developed code.**100**Kotsalos, C.; Latt, J.; Chopard, B. Bridging the Computational Gap between Mesoscopic and Continuum Modeling of Red Blood Cells for Fully Resolved Blood Flow.*J. Comput. Phys.*2019,*398*, 108905, DOI: 10.1016/j.jcp.2019.108905Google ScholarThere is no corresponding record for this reference.**101**Kotsalosa, C.; Latt, J.; Beny, J.; Chopard, B. Digital Blood in Massively Parallel CPU/GPU Systems for the Study of Platelet Transport.*Interface Focus*2019,*11*, 20190116, DOI: 10.1098/rsfs.2019.0116Google ScholarThere is no corresponding record for this reference.**102**Parkhurst, D. L.; Appelo, C. A. J.*Description of Input and Examples for PHREEQC Version 3 ─ A Computer Program for Speciation, Batch-Reaction , One-Dimensional Transport , and Inverse Geochemical Calculations*, U.S. Geological Survey Techniques and Methods, Book 6, Chapter A43, 2013, 6-43A, p 497; U.S. Geological Survey, 2013Google ScholarThere is no corresponding record for this reference.**103**Tetteh, J. T.; Alimoradi, S.; Brady, P. v.; Barati Ghahfarokhi, R. Electrokinetics at Calcite-Rich Limestone Surface: Understanding the Role of Ions in Modified Salinity Waterflooding.*J. Mol. Liq.*2020,*297*, 111868, DOI: 10.1016/j.molliq.2019.111868Google Scholar103https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFyrt7bO&md5=e0217208becbeafe29710c5a738d0c05Electrokinetics at calcite-rich limestone surface: Understanding the role of ions in modified salinity waterfloodingTetteh, Joel T.; Alimoradi, Sirwan; Brady, Patrick V.; Barati Ghahfarokhi, RezaJournal of Molecular Liquids (2020), 297 (), 111868CODEN: JMLIDT; ISSN:0167-7322. (Elsevier B.V.)Despite recent efforts to understand the wettability alteration process in limestone rocks during low salinity waterflooding, the findings related to wettability alteration due to changes in salinity and in the presence of a thin water film are still inconclusive. In this work, the effect of ions, temp. and soln. pH on the rock and oil surface charges were explored by measuring zeta potential and predicting it using double layer surface complexation modeling (SCM). SCM fitted the trends of the measured zeta potential for both rock-brine and oil-brine interf. by varying the surface equil. consts., particularly the equil. const. for the Ca2+ binding site. The SCM was used to predict 32 zeta potential values either measured exptl. or extd. from the literature. Zeta potential and isoelec. potential of rock-brine interface is dependent on brine salinity, sulfate concns. and soln. pH. Bond product sum (BPS), together with total disjoining pressure calcns. were used to predict wett. trends, supported by contact angle measur., where the total BPS was obsd. to be the lowest and repulsive disjoining pressure generated at the COBR for LSW brines. This was obsd. to be significantly influenced by [COOCa+][CO-3] and [COOMg+][CO-3] bond linkages at low pH (below 8) whiles the bond linkages of [COOCa+][CaCO-3] and [COOMg+][CaCO-3] dominated the COBR interface at higher pH (above 8).**104**Lutzenkirchen, J.*Surface Complexation Modelling*; Elsevier, 2006.Google ScholarThere is no corresponding record for this reference.**105**Tetteh, J. T.; Alimoradi, S.; Brady, P. V.; Barati, R. G. Electrokinetics at Calcite-Rich Limestone Surface: Understanding the Role of Ions in Modified Salinity Waterflooding.*J. Mol. Liq.*2020,*297*, 111868, DOI: 10.1016/j.molliq.2019.111868Google Scholar105https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFyrt7bO&md5=e0217208becbeafe29710c5a738d0c05Electrokinetics at calcite-rich limestone surface: Understanding the role of ions in modified salinity waterfloodingTetteh, Joel T.; Alimoradi, Sirwan; Brady, Patrick V.; Barati Ghahfarokhi, RezaJournal of Molecular Liquids (2020), 297 (), 111868CODEN: JMLIDT; ISSN:0167-7322. (Elsevier B.V.)Despite recent efforts to understand the wettability alteration process in limestone rocks during low salinity waterflooding, the findings related to wettability alteration due to changes in salinity and in the presence of a thin water film are still inconclusive. In this work, the effect of ions, temp. and soln. pH on the rock and oil surface charges were explored by measuring zeta potential and predicting it using double layer surface complexation modeling (SCM). SCM fitted the trends of the measured zeta potential for both rock-brine and oil-brine interf. by varying the surface equil. consts., particularly the equil. const. for the Ca2+ binding site. The SCM was used to predict 32 zeta potential values either measured exptl. or extd. from the literature. Zeta potential and isoelec. potential of rock-brine interface is dependent on brine salinity, sulfate concns. and soln. pH. Bond product sum (BPS), together with total disjoining pressure calcns. were used to predict wett. trends, supported by contact angle measur., where the total BPS was obsd. to be the lowest and repulsive disjoining pressure generated at the COBR for LSW brines. This was obsd. to be significantly influenced by [COOCa+][CO-3] and [COOMg+][CO-3] bond linkages at low pH (below 8) whiles the bond linkages of [COOCa+][CaCO-3] and [COOMg+][CaCO-3] dominated the COBR interface at higher pH (above 8).**106**Tetteh, J. T.; Alimoradi, S.; Brady, P. v.; Barati, R. G. Electrokinetics at Calcite-Rich Limestone Surface: Understanding the Role of Ions in Modified Salinity Waterflooding.*J. Mol. Liq.*2020,*297*, 111868, DOI: 10.1016/j.molliq.2019.111868Google Scholar106https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFyrt7bO&md5=e0217208becbeafe29710c5a738d0c05Electrokinetics at calcite-rich limestone surface: Understanding the role of ions in modified salinity waterfloodingTetteh, Joel T.; Alimoradi, Sirwan; Brady, Patrick V.; Barati Ghahfarokhi, RezaJournal of Molecular Liquids (2020), 297 (), 111868CODEN: JMLIDT; ISSN:0167-7322. (Elsevier B.V.)Despite recent efforts to understand the wettability alteration process in limestone rocks during low salinity waterflooding, the findings related to wettability alteration due to changes in salinity and in the presence of a thin water film are still inconclusive. In this work, the effect of ions, temp. and soln. pH on the rock and oil surface charges were explored by measuring zeta potential and predicting it using double layer surface complexation modeling (SCM). SCM fitted the trends of the measured zeta potential for both rock-brine and oil-brine interf. by varying the surface equil. consts., particularly the equil. const. for the Ca2+ binding site. The SCM was used to predict 32 zeta potential values either measured exptl. or extd. from the literature. Zeta potential and isoelec. potential of rock-brine interface is dependent on brine salinity, sulfate concns. and soln. pH. Bond product sum (BPS), together with total disjoining pressure calcns. were used to predict wett. trends, supported by contact angle measur., where the total BPS was obsd. to be the lowest and repulsive disjoining pressure generated at the COBR for LSW brines. This was obsd. to be significantly influenced by [COOCa+][CO-3] and [COOMg+][CO-3] bond linkages at low pH (below 8) whiles the bond linkages of [COOCa+][CaCO-3] and [COOMg+][CaCO-3] dominated the COBR interface at higher pH (above 8).**107**Molins, S.; Soulaine, C.; Prasianakis, N. I.; Abbasi, A.; Poncet, P.; Ladd, A. J. C.; Starchenko, V.; Roman, S.; Trebotich, D.; Tchelepi, H. A.; Steefel, C. I. Simulation of Mineral Dissolution at the Pore Scale with Evolving Fluid-Solid Interfaces: Review of Approaches and Benchmark Problem Set.*Comput. Geosci.*2020,*25*, 1285, DOI: 10.1007/s10596-019-09903-xGoogle ScholarThere is no corresponding record for this reference.**108**Tetteh, J. T.; Barati, R. Crude-Oil/Brine Interaction as a Recovery Mechanism for Low-Salinity Waterflooding of Carbonate Reservoirs.*SPE Reservoir Eval. Eng.*2019,*22*, 877, DOI: 10.2118/194006-paGoogle Scholar108https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmsF2iug%253D%253D&md5=56495586590226a905ee18c1b35322a2Crude-oil/brine interaction as a recovery mechanism for low-salinity waterflooding of carbonate reservoirsTetteh, Joel T.; Barati, RezaSPE Reservoir Evaluation & Engineering (2019), 22 (3), 877-896CODEN: SREEFG; ISSN:1930-0212. (Society of Petroleum Engineers)Low-salinity waterflooding in limestone formations has been less explored and hence less understood in enhanced-oil-recovery (EOR) literature. The mechanisms leading to improved recovery have been mostly attributed to wettability alteration, with less attention given to fluid/fluid-interaction mechanisms. In this work, we present a thorough investigation of the formation of water-in-oil microdispersions generated when low-salinity brine encounters crude oil and the suppressed snap-off effect caused by the presence of sulfate content in seawater-equiv.-salinity brines as recovery mechanisms in limestone rocks. Improved recovery by seawater brine was attributed to the changes in dynamic IFT measurement experienced using seawater brine as the continuous phase, compared with the use of LSW and formation-water-salinity (FWS) brine. Furthermore, the use of seawater as a displacing fluid succeeds in improving recovery because of its high surface elasticity suppressing the snap-off effect in the pore throat. We also present an easy and reliable mixing procedure representative of porous media, which could be used for screening brine and crude-oil samples for field application. Fluid/fluid interaction as well as high surface elasticity should be investigated as the causes of wettability alteration and improved recovery experienced by the use of LSW and seawater-salinity (SWS) brines interacting with limestone formations, resp.**109**Tetteh, J. T.; Alimoradi, S.; Brady, P. V.; Barati, R. G. Electrokinetics at the Limestone Surface: Understanding the Role of Ions in Modified Salinity Waterflooding.*J. Mol. Liq.*2020,*297*, 111868, DOI: 10.1016/j.molliq.2019.111868Google Scholar109https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFyrt7bO&md5=e0217208becbeafe29710c5a738d0c05Tetteh, Joel T.; Alimoradi, Sirwan; Brady, Patrick V.; Barati Ghahfarokhi, RezaJournal of Molecular Liquids (2020), 297 (), 111868CODEN: JMLIDT; ISSN:0167-7322. (Elsevier B.V.)**110**Hiorth, A.; Cathles, L. M.; Madland, M. V. The Impact of Pore Water Chemistry on Carbonate Surface Charge and Oil Wettability.*Transp. Porous Media*2010,*85*, 1– 21, DOI: 10.1007/s11242-010-9543-6Google Scholar110https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXhtFOnsbvE&md5=c4b7be80ec793f655012c5ec6fb00b7cThe Impact of Pore Water Chemistry on Carbonate Surface Charge and Oil WettabilityHiorth, A.; Cathles, L. M.; Madland, M. V.Transport in Porous Media (2010), 85 (1), 1-21CODEN: TPMEEI; ISSN:0169-3913. (Springer)Water chem. has been shown exptl. to affect the stability of water films and the sorption of org. oil components on mineral surfaces. When oil is displaced by water, water chem. has been shown to impact oil recovery. At least two mechanisms could account for these effects, the water chem. could change the charge on the rock surface and affect the rock wettability, and/or changes in the water chem. could dissolve rock minerals and affect the rock wettability. The explanations need not be the same for oil displacement of water as for water imbibition and displacement of oil. This article investigates how water chem. affects surface charge and rock dissoln. in a pure calcium carbonate rock similar to the Stevns Klint chalk by constructing and applying a chem. model that couples bulk aq. and surface chem. and also addresses mineral pptn. and dissoln. We perform calcns. for seawater and formation water for temps. between 70 and 130°C. The model we construct accurately predicts the surface potential of calcite and the adsorption of sulfate ions from the pore water. The surface potential changes are not able to explain the obsd. changes in oil recovery caused by changes in pore water chem. or temp. On the other hand, chem. dissoln. of calcite has the exptl. obsd. chem. and temp. dependence and could account for the exptl. recovery systematics. Based on this preliminary anal., we conclude that although surface potential may explain some aspects of the existing spontaneous imbibitions data set, mineral dissoln. appears to be the controlling factor.**111**Brady, P. V.; Krumhansl, J. L.; Mariner, P. E. Surface Complexation Modeling for Improved Oil Recovery. In*SPE Improved Oil Recovery Symposium*; Society of Petroleum Engineers, 2012; pp 14– 18.Google ScholarThere is no corresponding record for this reference.**112**Mahani, H.; Keya, A. L.; Berg, S.; Nasralla, R. Electrokinetics of Carbonate/Brine Interface in Low-Salinity Waterflooding: Effect of Brine Salinity, Composition, Rock Type, and PH on Zeta-Potential and a Surface-Complexation Model.*SPE J.*2017,*22*, 053– 068, DOI: 10.2118/181745-paGoogle Scholar112https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXjtVGmsLo%253D&md5=881b93f38ddc3b0aa447446d7cf161b7Electrokinetics of carbonate/brine interface in low-salinity waterflooding: effect of brine salinity, composition, rock type, and pH on ξ-potential and a surface-complexation modelMahani, Hassan; Keya, Arsene Levy; Berg, Steffen; Nasralla, RamezSPE Journal (Society of Petroleum Engineers) (2017), 22 (1), 53-68CODEN: SPJRFW; ISSN:1930-0220. (Society of Petroleum Engineers)Lab. studies have shown that wettability of carbonate rock can be altered to a less-oil-wetting state by manipulation of brine compn. and redn. of salinity. Our recent study (Mahani et al. 2015b) suggests that surface-charge alteration is likely to be the driving mechanism of the low-salinity effect in carbonates. Various studies have already established the sensitivity of carbonate- surface charge to brine salinity, pH value, and potential-detg. ions in brines. However, in the majority of the studies, single-salt brines or model-carbonate rocks have been used and it is fairly unclear how natural rock reacts to reservoir-relevant brine as well as successive brine diln.; whether different types of carbonate-reservoir rocks exhibit different electrokinetic properties; and how the surface-charge behavior obtained at different brine salinities and pH values can be explained. This paper presents a comparative study aimed at gaining more insight into the electrokinetics of different types of carbonate rock. This is achieved by ξ-potential measurements on Iceland spar calcite and three reservoir-related rocks-Middle Eastern limestone, Stevns Klint chalk, and Silurian dolomite outcrop-over a wide range of salinity, brine compn., and pH values. With a view to arriving at a more-tractable approach, a surface-complexation model (SCM) implemented in PHREEQC software (Parkhurst and Appelo 2013) is developed to relate our understanding of the surface reactions to measured ξ-potentials. It was found that regardless of the rock type, the trends of ξ-potentials with salinity and pH are quite similar. For all cases, the surface charge was found to be pos. in high-salinity formation water (FW), which should favor oil-wetting. The ξ-potential successively decreased toward neg. values when the brine salinity was lowered to seawater (SW) level and dild. SW. At all salinities, the ξ-potential showed a strong dependence on pH, with pos. slope that remained so even with excessive diln. The sensitivity of the ξ-potential to pH change was often higher at lower salinities. The existing SCMs cannot predict the obsd. increase of ξ-potential with pH; therefore, a new model is proposed to capture this feature. According to modeling results, formation of surface species, particularly >CaSO4 and to a lower extent >CO4Ca and >CO4Mg, strongly influence the total surface charge. Increasing the pH turns the neg. charged moiety >CaSO4 into both neg. charged >CaCO3 and neutral>CaOH entities. (Note that throughout this paper, the symbol>indicates surface complexes.) This substitution reduces the neg. charge of the surface. The surface concn. of >CO3Ca and >COMg moieties changes little with change of pH. Nevertheless, besides similarities in ξ-potential trends, there exist notable differences in terms of magnitude and the isoelec. point (IEP), even between carbonates that are mainly composed of calcite. Among all the samples, chalk particles exhibited the most neg. surface charges, followed by limestone. In contrast to this, dolomite particles showed the most pos. ξ-potential, followed by calcite crystal. Overall, chalk particles exhibited the highest surface reactivity to pH and salinity change, whereas dolomite particles showed the lowest.**113**Tagavifar, M.; Jang, S. H.; Sharma, H.; Wang, D.; Chang, L. Y.; Mohanty, K.; Pope, G. A. Effect of PH on Adsorption of Anionic Surfactants on Limestone: Experimental Study and Surface Complexation Modeling.*Colloids Surf., A*2018,*538*, 549– 558, DOI: 10.1016/j.colsurfa.2017.11.050Google Scholar113https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhvVKlsr3M&md5=4156005d433d14918aee6762dd50a9dbEffect of pH on adsorption of anionic surfactants on limestone: Experimental study and surface complexation modelingTagavifar, M.; Jang, S. H.; Sharma, H.; Wang, D.; Chang, L. Y.; Mohanty, K.; Pope, G. A.Colloids and Surfaces, A: Physicochemical and Engineering Aspects (2018), 538 (), 549-558CODEN: CPEAEH; ISSN:0927-7757. (Elsevier B.V.)We investigate surfactant adsorption on a model carbonate rock over a wide range of pH and surfactant-to-solid ratios, by both an exptl. and a theor. approach, to obtain a quant. understanding of how mineral constituents affect the adsorption equil. and dynamics. To constrain and compare the relative adsorption affinity and the likely modes of attachment on mineral constituents as pH changes, we performed surface complexation calcns. using a two-surface multisite diffuse layer model. We propose the formation of two surface species on both the major (i.e., calcite) and trace (i.e., the oxide-like sites on the edges of clay platelets) minerals: a monodentate inner-sphere complex and a weak or hydrogen bonding complex. Our modeling results suggest that charge-regulated inner-sphere complexation is the dominant adsorption mechanism on the calcite and oxide-like sites at low pH values regardless of the surface loading. We found weak or hydrogen bond adsorption to be significant on the calcite surface, and this became the dominant adsorption mode at pH ∼10. While the adsorption on calcite increases with surface loading, adsorption on the oxide-like sites remains independent of surface loading. These results suggest that surfactant adsorption can be comparable on the abundant low-surface-area calcite and trace high-surface-area oxide-like sites.**114**Sanaei, A.; Tavassoli, S.; Sepehrnoori, K. Investigation of Modified Water Chemistry for Improved Oil Recovery: Application of DLVO Theory and Surface Complexation Model.*Colloids Surf., A*2019,*574*, 131– 145, DOI: 10.1016/j.colsurfa.2019.04.075Google Scholar114https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXosl2nsb4%253D&md5=afb5dfd43335963a549be9ee5fb918f9Investigation of modified Water chemistry for improved oil recovery: Application of DLVO theory and surface complexation modelSanaei, Alireza; Tavassoli, Shayan; Sepehrnoori, KamyColloids and Surfaces, A: Physicochemical and Engineering Aspects (2019), 574 (), 131-145CODEN: CPEAEH; ISSN:0927-7757. (Elsevier B.V.)It is widely accepted that oil recovery during waterflooding can be improved by modifying the compn. of the injected brine. A typical approach is dilg. the formation water to a specific lower salinity. However, recent exptl. studies report the adverse effect of formation water diln. on oil recovery for specific oil/brine/rock systems. The adverse effect depends on the interactions within the system and is more pronounced in carbonates. In this study, we investigated the effect of water compn. on the performance of low salinity water injection by considering the complex interaction of oil, brine, and rock system. A surface complexation model (SCM) is developed to calc. the zeta-potential at oil and rock surfaces. Considering a water film between oil and rock and using DLVO theory, attractive/repulsive forces between oil/brine and brine/rock interfaces are calcd. Contact angle is predicted employing the augmented Young-Laplace equation. Our zeta potential calcns. based on the SCM reproduced the exptl. data of oil/brine and brine/calcite zeta potential measurements. Our contact angle calcns. using the DLVO theory and the augmented Young-Laplace equation accurately estd. the dynamic trend of contact angle during low salinity flood. Modeling wettability alteration as a function of contact angle was sufficient to predict the low salinity effect. The developed model is implemented in a comprehensive compositional reactive transport simulator to validate the proposed approach.

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**1**Green, D. W.; Willhite, G. P.;*Enhanced Oil Recovery*; Henry L. Doherty Memorial Fund of AIME, Society of Petroleum Engineers, 1998; Vol. 6.There is no corresponding record for this reference.**2**Gluyas, J.; Mathias, S.*Geological Storage of Carbon Dioxide (CO*; Elsevier, 2013._{2}): Geoscience, Technologies, Environmental Aspects and Legal FrameworksThere is no corresponding record for this reference.**3**Sheng, J. J. Critical Review of Low-Salinity Waterflooding.*J. Pet. Sci. Eng.*2014,*120*, 216– 224, DOI: 10.1016/j.petrol.2014.05.0263https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtVaktb7I&md5=06360c1babcae3318a17a1ec9901eecaCritical review of low-salinity waterfloodingSheng, J. J.Journal of Petroleum Science & Engineering (2014), 120 (), 216-224CODEN: JPSEE6; ISSN:0920-4105. (Elsevier B.V.)It was obsd. that higher oil recovery could be obtained when low-salinity (LS) water flooded a core of high-salinity initial water about 15 years ago. Such low-salinity waterflooding benefit or effect has drawn the oil industry attention since then. In the recent years, many researchers conducted lab. corefloods, and several companies carried field tests. The objectives of these efforts were (1) to conform the benefits and (2) find the mechanisms of such benefit. Although most of the results confirmed the pos. effect, some results showed no benefit. Many mechanisms have been proposed, but there is no consensus of the dominant mechanism(s). The oil industry is continuing the effort to discover the effect. This paper is to provide a crit. review of the results and to summarize the achievements of the industry's effort. This paper aims to provide the status of the art. The information provided in this paper hopefully will help to speed up our further efforts to explore this effect. The following contents are reviewed: (1) history of low-salinity waterflooding; (2) lab. observations; (3) field observations; (4) working conditions of low-salinity effect; (5) mechanisms of low-salinity waterflooding; and (6) simulation of low-salinity waterflooding.In this paper, the mechanisms proposed in the literature and their validity are discussed.**4**Lager, A.; Webb, K. J.; Black, C. J. J.; Singleton, M.; Sorbie, K. S. Low Salinity Oil Recovery - An Experimental Investigation.*Petrophysics*2008,*49*, 28– 35There is no corresponding record for this reference.**5**Willhite, G. P.*Waterflooding*; Society of Petroleum Engineers: Richardson, TX, 1986.There is no corresponding record for this reference.**6**Kazemi Nia Korrani, A.; Jerauld, G. R.; Sepehrnoori, K. Coupled Geochemical-Based Modeling of Low Salinity Waterflooding.*SPE Improved Oil Recovery Symposium*, 2014, No. 2008; Vol. 1–23.There is no corresponding record for this reference.**7**Shalabi, E. W. A. Modeling the Effect of Injecting Low Salinity Water on Oil Recovery from Carbonate Reservoirs, Dissertation, The University of Texas at Austin, 2014.There is no corresponding record for this reference.**8**Sanaei, A. Compositional Reactive-Transport Modeling of Engineered Waterflooding, Dissertation, University of Texas at Austin, 2019.There is no corresponding record for this reference.**9**Sanaei, A.; Tavassoli, S.; Sepehrnoori, K. Investigation of Modified Water Chemistry for Improved Oil Recovery: Application of DLVO Theory and Surface Complexation Model.*Colloids Surf., A*2019,*574*, 131– 145, DOI: 10.1016/j.colsurfa.2019.04.0759https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXosl2nsb4%253D&md5=afb5dfd43335963a549be9ee5fb918f9Investigation of modified Water chemistry for improved oil recovery: Application of DLVO theory and surface complexation modelSanaei, Alireza; Tavassoli, Shayan; Sepehrnoori, KamyColloids and Surfaces, A: Physicochemical and Engineering Aspects (2019), 574 (), 131-145CODEN: CPEAEH; ISSN:0927-7757. (Elsevier B.V.)It is widely accepted that oil recovery during waterflooding can be improved by modifying the compn. of the injected brine. A typical approach is dilg. the formation water to a specific lower salinity. However, recent exptl. studies report the adverse effect of formation water diln. on oil recovery for specific oil/brine/rock systems. The adverse effect depends on the interactions within the system and is more pronounced in carbonates. In this study, we investigated the effect of water compn. on the performance of low salinity water injection by considering the complex interaction of oil, brine, and rock system. A surface complexation model (SCM) is developed to calc. the zeta-potential at oil and rock surfaces. Considering a water film between oil and rock and using DLVO theory, attractive/repulsive forces between oil/brine and brine/rock interfaces are calcd. Contact angle is predicted employing the augmented Young-Laplace equation. Our zeta potential calcns. based on the SCM reproduced the exptl. data of oil/brine and brine/calcite zeta potential measurements. Our contact angle calcns. using the DLVO theory and the augmented Young-Laplace equation accurately estd. the dynamic trend of contact angle during low salinity flood. Modeling wettability alteration as a function of contact angle was sufficient to predict the low salinity effect. The developed model is implemented in a comprehensive compositional reactive transport simulator to validate the proposed approach.**10**Chen, S.; Doolen, G. D. Lattice Boltzmann Method for Fluid Flows.*Annu. Rev. Fluid. Mech.*1998,*30*, 329– 364, DOI: 10.1146/annurev.fluid.30.1.329There is no corresponding record for this reference.**11**Shan, X.; Chen, H. Lattice Boltzmann Model for Simulating Flows with Multiple Pahses and Components.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*1993,*47*, 1815, DOI: 10.1103/physreve.47.181511https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2sfitlKqsg%253D%253D&md5=e8db837f08d4b5478778d975e30c6440Lattice Boltzmann model for simulating flows with multiple phases and componentsShan; ChenPhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics (1993), 47 (3), 1815-1819 ISSN:1063-651X.There is no expanded citation for this reference.**12**Huang, H.; Sukop, M. C.; Lu, X.-Y.*Multiphase Lattice Boltzmann Methods: Theory and Application*, 1st ed.; WILEY Blackwell, 2015.There is no corresponding record for this reference.**13**Yoon, H.; Kang, Q.; Valocchi, A. J. 12. Lattice Boltzmann-Based Approaches for Pore-Scale Reactive Transport.*Rev. Mineral. Geochem.*2015,*80*, 393– 432, DOI: 10.1515/9781501502071-012There is no corresponding record for this reference.**14**Sethian, J. A. A. Fast Marching Level Set Method for Monotonically Advancing Fronts.*Proc. Natl. Acad. Sci. U.S.A.*1996,*93*, 1591, DOI: 10.1073/pnas.93.4.159114https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28Xht1entrs%253D&md5=183b90083b120cd611670a2edea652b8A fast marching level set method for monotonically advancing frontsSethian, J. A.Proceedings of the National Academy of Sciences of the United States of America (1996), 93 (4), 1591-5CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)A fast marching level set method is presented for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential equation for a propagating level set function and use techniques borrowed from hyperbolic conservation laws. Topol. changes, corner and cusp development, and accurate detn. of geometric properties such as curvature and normal direction are naturally obtained in this setting. This paper describes a particular case of such methods for interfaces whose speed depends only on local position. The technique works by coupling work on entropy conditions for interface motion, the theory of viscosity solns. for Hamilton-Jacobi equations, and fast adaptive narrow band level set methods. The technique is applicable to a variety of problems, including shape-from-shading problems, lithog. development calcns. in microchip manufg., and arrival time problems in control theory.**15**Tryggvason, G.; Bunner, B.; Esmaeeli, A.; Juric, D.; Al-Rawahi, N.; Tauber, W.; Han, J.; Nas, S.; Jan, Y. J. A Front-Tracking Method for the Computations of Multiphase Flow.*J. Comput. Phys.*2001,*169*, 708– 759, DOI: 10.1006/jcph.2001.672615https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXktlOjs7c%253D&md5=9ecd5956190cd28843a8935bc8eda10eA Front-Tracking Method for the Computations of Multiphase FlowTryggvason, G.; Bunner, B.; Esmaeeli, A.; Juric, D.; Al-Rawahi, N.; Tauber, W.; Han, J.; Nas, S.; Jan, Y.-J.Journal of Computational Physics (2001), 169 (2), 708-759CODEN: JCTPAH; ISSN:0021-9991. (Academic Press)Direct numerical simulations of multiphase flows, using a front-tracking method, are presented. The method is based on writing one set of governing equations for the whole computational domain and treating the different phases as one fluid with variable material properties. Interfacial terms are accounted for by adding the appropriate sources as δ functions at the boundary sepg. the phases. The unsteady Navier-Stokes equations are solved by a conventional finite vol. method on a fixed, structured grid and the interface, or front, is tracked explicitly by connected marker points. Interfacial source terms such as surface tension are computed on the front and transferred to the fixed grid. Advection of fluid properties such as d. is done by following the motion of the front. The method has been implemented for fully three-dimensional flows, as well as for two-dimensional and axisym. ones. First, the method is described for the flow of two or more isothermal phases. The representation of the moving interface and its dynamic restructuring, as well as the transfer of information between the moving front and the fixed grid, are discussed. Applications and extensions of the method to homogeneous bubbly flows, atomization, flows with variable surface tension, solidification, and boiling are then presented. (c) 2001 Academic Press.**16**Hirt, C. W.; Nichols, B. D. Volume of Fluid (VOF) Method for the Dynamics of Free Boundaries.*J. Comput. Phys.*1981,*39*, 201, DOI: 10.1016/0021-9991(81)90145-5There is no corresponding record for this reference.**17**Shan, X.; Chen, H. Simulation of Nonideal Gases and Liquid-Gas Phase Transitions by the Lattice Boltzmann Equation.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*1994,*49*, 2941– 2948, DOI: 10.1103/physreve.49.294117https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXjtVyjs74%253D&md5=4052dca0b659c3823de4fc0d6198a17eSimulation of nonideal gases and liquid-gas phase transitions by the lattice Boltzmann equationShan, Xiaowen; Chen, HudongPhysical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics (1994), 49 (4-A), 2941-8CODEN: PLEEE8; ISSN:1063-651X.The authors describe in detail a recently proposed lattice-Boltzmann model (S. and C., 1993) for simulating flows with multiple phases and components. In particular, the focus is on the modeling of one-component fluid systems, which obey nonideal-gas equations of state, and can undergo a liq.-gas-type phase transition. The model is shown to be momentum conserving. From the microscopic mech. stability condition, the densities in the bulk liq. and gas phases were obtained as functions of a temp.-like parameter. Comparisons with the thermodn. theory of phase transitions showed that the lattice-Boltzmann-equation model can be made to correspond exactly to an isothermal process. The d. profile in the liq.-gas interface was also obtained as a function of the temp.-like parameter, and is shown to be isotropic. The surface tension was calcd., which can be changed independently. The anal. conclusions are verified by numerical simulations.**18**Gunstensen, A. K.; Rothman, D. H.; Zaleski, S.; Zanetti, G. Lattice Boltzmann Model of Immiscible Fluids.*Phys. Rev. A: At., Mol., Opt. Phys.*1991,*43*, 4320– 4327, DOI: 10.1103/physreva.43.432018https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3MXisFCgt7Y%253D&md5=fded053cb95d27ee2f10cabfee5267caLattice Boltzmann model of immiscible fluidsGunstensen, Andrew K.; Rothman, Daniel H.; Zaleski, Stephane; Zanetti, GianluigiPhysical Review A: Atomic, Molecular, and Optical Physics (1991), 43 (8), 4320-7CODEN: PLRAAN; ISSN:0556-2791.A lattice Boltzmann model is introduced for simulating immiscible binary fluids in two dimensions. The model, based on the Boltzmann equation of lattice-gas hydrodynamics, incorporates features of a previously introduced discrete immiscible lattice-gas model. A theor. value of the surface-tension coeff. is derived and found to be in excellent agreement with values obtained from simulations. The model serves as a numerical method for the simulation of immiscible two-phase flow; a preliminary application illustrates a simulation of flow in a two-dimensional microscopic model of a porous medium. Extension of the model to three dimensions appears straightforward.**19**Swift, M. R.; Osborn, W. R.; Yeomans, J. M. Lattice Boltzmann Simulation of Nonideal Fluids.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*1995,*75*, 830, DOI: 10.1103/physrevlett.75.830There is no corresponding record for this reference.**20**Swift, M. R.; Orlandini, E.; Osborn, W. R.; Yeomans, J. M. Lattice Boltzmann Simulations of Liquid-Gas and Binary Fluid Systems.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*1996,*54*, 5041– 5052, DOI: 10.1103/physreve.54.504120https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28XntFehu7o%253D&md5=d1497755998822f2393c1471140a9ae6Lattice Boltzmann simulations of liquid-gas and binary fluid systemsSwift, Michael R.; Orlandini, E.; Osborn, W. R.; Yeomans, J. M.Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics (1996), 54 (5), 5041-5052CODEN: PLEEE8; ISSN:1063-651X. (American Physical Society)The authors present the details of a lattice Boltzmann approach to phase sepn. in nonideal one- and two-component fluids. The collision rules are chosen such that the equil. state corresponds to an input free energy and the bulk flow is governed by the continuity, Navier-Stokes, and, for the binary fluid, a convection-diffusion equation. Numerical results are compared to simple analytic predictions to confirm that the equil. state is indeed thermodynamically consistent and that the kinetics of the approach to equil. lie within the expected universality classes. The approach is compared to other lattice Boltzmann simulations of nonideal systems.**21**He, X.; Chen, S.; Zhang, R. A Lattice Boltzmann Scheme for Incompressible Multiphase Flow and Its Application in Simulation of Rayleigh-Taylor Instability.*J. Comput. Phys.*1999,*152*, 642– 663, DOI: 10.1006/jcph.1999.625721https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1MXjvV2qsLk%253D&md5=147bf7f822eafb9e8e80b14d84ca319eA Lattice Boltzmann Scheme for Incompressible Multiphase Flow and Its Application in Simulation of Rayleigh-Taylor InstabilityHe, Xiaoyi; Chen, Shiyi; Zhang, RaoyangJournal of Computational Physics (1999), 152 (2), 642-663CODEN: JCTPAH; ISSN:0021-9991. (Academic Press)A new lattice Boltzmann scheme is proposed for simulation of multiphase flow in the nearly incompressible limit. The new scheme simulates fluid flows based on distribution functions. The interfacial dynamics, such as phase segregation and surface tension, are modeled by incorporating mol. interactions. The lattice Boltzmann equations are derived from the continuous Boltzmann equation with appropriate approxns. suitable for incompressible flow. The numerical stability is improved by reducing the effect of numerical errors in calcn. of mol. interactions. An index function is used to track interfaces between different phases. Simulations of the two-dimensional Rayleigh-Taylor instability yield satisfactory results. The interface thickness is maintained at 3-4 grid spacings throughout simulations without artificial reconstruction steps. (c) 1999 Academic Press.**22**Parmigiani, A. Lattice Boltzmann Calculations of Reactive Multiphase Flows in Porous Media, Thesis, University of Geneva, 2011; Vol. 129.There is no corresponding record for this reference.**23**Kingdon, R. D.; Schofield, P. A Reaction-Flow Lattice Boltzmann Model.*J. Phys. A: Math. Gen.*1992,*25*, L907– L910, DOI: 10.1088/0305-4470/25/14/008There is no corresponding record for this reference.**24**Kang, Q.; Zhang, D.; Chen, S. Displacement of a Three-Dimensional Immiscible Droplet in a Duct.*J. Fluid Mech.*2005,*545*, 41– 66, DOI: 10.1017/s0022112005006956There is no corresponding record for this reference.**25**Kang, Q.; Lichtner, P. C.; Zhang, D. Lattice Boltzmann Pore-Scale Model for Multicomponent Reactive Transport in Porous Media.*J. Geophys. Res.: Solid Earth*2006,*111*, 1– 12, DOI: 10.1029/2005jb003951There is no corresponding record for this reference.**26**Liu, M.; Mostaghimi, P. Pore-Scale Simulation of Dissolution-Induced Variations in Rock Mechanical Properties.*Int. J. Heat Mass Transfer*2017,*111*, 842– 851, DOI: 10.1016/j.ijheatmasstransfer.2017.04.04926https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXmtlOmtr8%253D&md5=d1d969aa785e169426ccf02b87a01419Pore-scale simulation of dissolution-induced variations in rock mechanical propertiesLiu, Min; Mostaghimi, PeymanInternational Journal of Heat and Mass Transfer (2017), 111 (), 842-851CODEN: IJHMAK; ISSN:0017-9310. (Elsevier Ltd.)Reactive transport is simulated on rock geometries to explore the variation of mech. properties of porous media. A numerical framework is developed to model reactive transport in sandstones and carbonates. Fluid flow, solute transport and chem. reactions are simulated directly on micro-CT images. Porosity profiles along the flow direction are computed during the dissoln. to describe the change in the pore structures. Stress load cases on porous media are simulated and maps of deformation in the rocks are compared. Uniform deformation is obsd. in high Pe´clet regimes. Young's modulus and Poisson's ratio are calcd. sep. for a range of Pe´clet and Damkohler regimes. The findings show that mech. properties in low Pe´clet regimes are more sensitive to porosity variations. For the same reaction regime, carbonates present a lower decrease in Young's modulus after reactions but a higher decline in Poisson's ratio in comparison with sandstones. This work reveals the strong dependency of mech. properties on Pe´clet and Damkohler regimes in reactive transport.**27**Liu, M.; Mostaghimi, P. High-Resolution Pore-Scale Simulation of Dissolution in Porous Media.*Chem. Eng. Sci.*2017,*161*, 360– 369, DOI: 10.1016/j.ces.2016.12.06427https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1Gqsw%253D%253D&md5=81ea06e636080870dbd5cc84b33b782dHigh-resolution pore-scale simulation of dissolution in porous mediaLiu, Min; Mostaghimi, PeymanChemical Engineering Science (2017), 161 (), 360-369CODEN: CESCAC; ISSN:0009-2509. (Elsevier Ltd.)Reactive flow is imperative in a wide range of chem. sciences, hydrogeol. and environmental applications. A parallel numerical framework is presented for modeling the dissoln. of a carbonate rock at the pore scale. Mass transport, chem. reactions, solid updates and migration are included in the model which are solved by the combination of lattice Boltzmann and finite vol. methods. For calcn. of the flow field, the incompressible Stokes equation is solved by applying an efficient lattice Boltzmann method with the D3Q19 scheme. The solid-fluid interaction is computed with the finite vol. method. The numerical method includes the migration of solid particles released due to dissoln. within the porous medium. The solid migration is realized by the cluster anal. and local movement. We validate this model by comparing against published dynamic micro-CT imaging expts. for dissoln. of a Ketton carbonate. To measure the local dissoln., the porosity profiles are compared with the published exptl. observations. The increases in permeability and porosity are investigated and a power law is derived to describe their relationship. Then, the significance of capturing the migration of solid particles released due to dissoln. on hydrol. properties of rocks is explored. The numerical approach is able to perform parallel simulation on large high-resoln. micro-CT images. We show the importance of simulation directly on micro-CT images without reducing the resoln. of rock micro-CT images. Further simulations are performed at Peclet regimes similar to sub-surface flow and the effect of flow rate on reactive transport is studied. This study illustrates the effect of inclusion of solid migration and the capability of simulation of reactive transport directly on high-resoln. images and helps understand the reactive transport at the pore scale.**28**Tian, Z.; Wang, J. Lattice Boltzmann Simulation of CO2 Reactive Transport in Network Fractured Media.*Water Resour. Res.*2017,*53*, 7366– 7381, DOI: 10.1002/2017wr02106328https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsFWhurzF&md5=5188a687e295b3bcc3b6c6c0e5ad8614Lattice Boltzmann simulation of CO2 reactive transport in network fractured mediaTian, Zhiwei; Wang, JunyeWater Resources Research (2017), 53 (8), 7366-7381CODEN: WRERAQ; ISSN:0043-1397. (Wiley-Blackwell)Carbon dioxide (CO2) geol. sequestration plays an important role in mitigating CO2 emissions for climate change. Understanding interactions of the injected CO2 with network fractures and hydrocarbons is key for optimizing and controlling CO2 geol. sequestration and evaluating its risks to ground water. However, there is a well-known, difficult process in simulating the dynamic interaction of fracture-matrix, such as dynamic change of matrix porosity, unsatd. processes in rock matrix, and effect of rock mineral properties. In this paper, we develop an explicit model of the fracture-matrix interactions using multilayer bounce-back treatment as a first attempt to simulate CO2 reactive transport in network fractured media through coupling the Dardis's LBM porous model for a new interface treatment. Two kinds of typical fracture networks in porous media are simulated: straight cross network fractures and interleaving network fractures. The reaction rate and porosity distribution are illustrated and well-matched patterns are found. The species concn. distribution and evolution with time steps are also analyzed and compared with different transport properties. The results demonstrate the capability of this model to investigate the complex processes of CO2 geol. injection and reactive transport in network fractured media, such as dynamic change of matrix porosity.**29**Di Palma, P. R.; Huber, C.; Viotti, P. A New Lattice Boltzmann Model for Interface Reactions between Immiscible Fluids.*Adv. Water Resour.*2015,*82*, 139– 149, DOI: 10.1016/j.advwatres.2015.05.00129https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXosVKksLY%253D&md5=ae4b020a394dc4f23167fc5d4ee71b4bA new lattice Boltzmann model for interface reactions between immiscible fluidsDi Palma, Paolo Roberto; Huber, Christian; Viotti, PaoloAdvances in Water Resources (2015), 82 (), 139-149CODEN: AWREDI; ISSN:0309-1708. (Elsevier Ltd.)In this paper, we describe a lattice Boltzmann model to simulate chem. reactions taking place at the interface between two immiscible fluids. The phase-field approach is used to identify the interface and its orientation, the concn. of reactant at the interface is then calcd. iteratively to impose the correct reactive flux condition. The main advantages of the model is that interfaces are considered part of the bulk dynamics with the corrective reactive flux introduced as a source/sink term in the collision step, and, as a consequence, the model's implementation and performance is independent of the interface geometry and orientation. Results obtained with the proposed model are compared to anal. soln. for three different benchmark tests (stationary flat boundary, moving flat boundary and dissolving droplet). We find an excellent agreement between anal. and numerical solns. in all cases. Finally, we present a simulation coupling the Shan Chen multiphase model and the interface reactive model to simulate the dissoln. of a collection of immiscible droplets with different sizes rising by buoyancy in a stagnant fluid.**30**Shan, X. Simulation of Rayleigh-Bénard convection using a lattice Boltzmann method.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*1997,*55*, 2780– 2788, DOI: 10.1103/physreve.55.278030https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXitVKitb4%253D&md5=7c13e835a4ec9796e12adf4010f5e707Simulation of Rayleigh-Benard convection using a lattice Boltzmann methodShan, XiaowenPhysical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics (1997), 55 (3-A), 2780-2788CODEN: PLEEE8; ISSN:1063-651X. (American Physical Society)Rayleigh-Benard convection is numerically simulated in two and three dimensions using a recently developed two-component lattice Boltzmann equation (LBE) method. The d. field of the second component, which evolves according to the advection-diffusion equation of a passive scalar, is used to simulate the temp. field. A body force proportional to the temp. is applied, and the system satisfies the Boussinesq equation except for a slight compressibility. A no-slip, isothermal boundary condition is imposed in the vertical direction, and periodic boundary conditions are used in horizontal directions. The crit. Rayleigh no. for the onset of the Rayleigh-Benard convection agrees with the theor. prediction. As the Rayleigh no. is increased higher, the steady two-dimensional convection rolls become unstable. The wavy instability and aperiodic motion obsd., as well as the Nusselt no. as a function of the Rayleigh no., are in good agreement with exptl. observations and theor. predictions. The LBE model is found to be efficient accurate, and numerically stable for the simulation of fluid flows with heat and mass transfer.**31**Huber, C.; Parmigiani, A.; Chopard, B.; Manga, M.; Bachmann, O. Lattice Boltzmann Model for Melting with Natural Convection.*Int. J. Heat Fluid Flow*2008,*29*, 1469– 1480, DOI: 10.1016/j.ijheatfluidflow.2008.05.00231https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtFCksLvN&md5=e2a1c69e82786792d4ff6ab58b2996dcLattice Boltzmann model for melting with natural convectionHuber, Christian; Parmigiani, Andrea; Chopard, Bastien; Manga, Michael; Bachmann, OlivierInternational Journal of Heat and Fluid Flow (2008), 29 (5), 1469-1480CODEN: IJHFD2; ISSN:0142-727X. (Elsevier B.V.)We develop a lattice Boltzmann method to couple thermal convection and pure-substance melting. The transition from conduction-dominated heat transfer to fully-developed convection is analyzed and scaling laws and previous numerical results are reproduced by our numerical method. We also investigate the limit in which thermal inertia (high Stefan no.) cannot be neglected. We use our results to extend the scaling relations obtained at low Stefan no. and establish the correlation between the melting front propagation and the Stefan no. for fully-developed convection. We conclude by showing that the model presented here is particularly well-suited to study convection melting in geometrically complex media with many applications in geosciences.**32**Yoon, H.; Kang, Q.; Valocchi, A. J. 12. Lattice Boltzmann-Based Approaches for Pore-Scale Reactive Transport.*Rev. Mineral. Geochem.*2015,*80*, 393– 432, DOI: 10.1515/9781501502071-012There is no corresponding record for this reference.**33**Luan, H. B.; Xu, H.; Chen, L.; Sun, D. L.; Tao, W. Q. Numerical Illustrations of the Coupling Between the Lattice Boltzmann Method and Finite-Type Macro-Numerical Methods.*Numer. Heat Transfer, Part B*2010,*57*, 147– 171, DOI: 10.1080/1542140090357992933https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkslKqtLY%253D&md5=ad7bf3ecff067445e9d3c7f78a8be782Numerical illustrations of the coupling between the lattice Boltzmann method and finite-type macro-numerical methodsLuan, H. B.; Xu, H.; Chen, L.; Sun, D. L.; Tao, W. Q.Numerical Heat Transfer, Part B: Fundamentals (2010), 57 (2), 147-171CODEN: NHBFEE; ISSN:1040-7790. (Taylor & Francis, Inc.)An analytic expression called a reconstruction operator is proposed for the exchange from velocity of finite-type methods to the single-particle distribution function of the lattice Boltzmann method (LBM). The combined finite-vol. method and lattice Boltzmann method (called the CFVLBM) is adopted to solve three flow cases, backward-facing flow, flow around a circular cylinder, and lid-driven cavity flow. The results predicted by the CFVLBM agree with the available numerical solns. very well. It is shown that the vorticity contour distribution is a more appropriate parameter to ensure good smoothness and consistency at the coupling interface. At the same time, CPU time used by the CFVLBM(II), with more than one outer iteration before interface information exchange, is much less than that of the CFVLBM(I), where interface information exchanges are executed after each outer iteration.**34**Yu, D.; Mei, R.; Shyy, W. A Multi-Block Lattice Boltzmann Method for Viscous Fluid Flows.*Int. J. Numer. Methods Fluids*2002,*39*, 99– 120, DOI: 10.1002/fld.280There is no corresponding record for this reference.**35**Luan, H.-B.; Xu, H.; Chen, L.; Sun, D.-L.; He, Y.-L.; Tao, W.-Q. Evaluation of the Coupling Scheme of FVM and LBM for Fluid Flows around Complex Geometries.*Int. J. Heat Mass Transfer*2011,*54*, 1975– 1985, DOI: 10.1016/j.ijheatmasstransfer.2011.01.004There is no corresponding record for this reference.**36**Chen, L.; He, Y.-L.; Kang, Q.; Tao, W.-Q. Coupled Numerical Approach Combining Finite Volume and Lattice Boltzmann Methods for Multi-Scale Multi-Physicochemical Processes.*J. Comput. Phys.*2013,*255*, 83– 105, DOI: 10.1016/j.jcp.2013.07.03436https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFOgu7%252FJ&md5=ea0a7173f9b41b18f124bb2db4c0e3a4Coupled numerical approach combining finite volume and lattice Boltzmann methods for multi-scale multi-physicochemical processesChen, Li; He, Ya-Ling; Kang, Qinjun; Tao, Wen-QuanJournal of Computational Physics (2013), 255 (), 83-105CODEN: JCTPAH; ISSN:0021-9991. (Elsevier Inc.)A coupled (hybrid) simulation strategy spatially combining the finite vol. method (FVM) and the lattice Boltzmann method (LBM), called CFVLBM, is developed to simulate coupled multi-scale multi-physicochem. processes. In the CFVLBM, computational domain of multi-scale problems is divided into two sub-domains, i.e., an open, free fluid region and a region filled with porous materials. The FVM and LBM are used for these two regions, resp., with information exchanged at the interface between the two sub-domains. A general reconstruction operator (RO) is proposed to derive the distribution functions in the LBM from the corresponding macro scalar, the governing equation of which obeys the convection-diffusion equation. The CFVLBM and the RO are validated in several typical physicochem. problems and then are applied to simulate complex multi-scale coupled fluid flow, heat transfer, mass transport, and chem. reaction in a wall-coated micro reactor. The max. ratio of the grid size between the FVM and LBM regions is explored and discussed.**37**Sullivan, S. P.; Sani, F. M.; Johns, M. L.; Gladden, L. F. Simulation of Packed Bed Reactors Using Lattice Boltzmann Methods.*Chem. Eng. Sci.*2005,*60*, 3405– 3418, DOI: 10.1016/j.ces.2005.01.03837https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXjs1Kqtrc%253D&md5=c7f16251939208cb87ef397881b7a849Simulation of packed bed reactors using lattice Boltzmann methodsSullivan, S. P.; Sani, F. M.; Johns, M. L.; Gladden, L. F.Chemical Engineering Science (2005), 60 (12), 3405-3418CODEN: CESCAC; ISSN:0009-2509. (Elsevier Ltd.)Lattice Boltzmann (LB) methods are used to simulate hydrodynamics, reaction and subsequent mass transfer in a disordered packed bed of catalyst particles at sub-pore length-scales. In contrast to previous studies, a variety of modifications are introduced in the LB method enabling particle Peclet nos. ≤ 108, and hence realistic values of diffusivity, to be accessed. These include decoupling the hydrodynamics from mass transfer and the use of a rest fraction in the LB formulation of mass transfer. In addn. the mass transfer simulations are modified to permit spatially varying values of diffusivity, essential to differentiate between intra- and inter-particle diffusivity (Dintra and Dinter, resp.). The simulation method is applied to both a disordered and ordered 2D packing for a range of Peclet (15.6-1557.8) and Reynolds (0.16-1.56) nos., as well as various ratios of Dintra/Dinter (0-1), while simulating an esterification reaction catalyzed by an ion-exchange resin. The value of D intra is found to have limited effect, while reducing Peclet no. results in a considerable increase in overall conversion. The simulation method is then applied to a 3D lattice for which exptl. conversion data is available. This exptl. data is straddled by the simulation case of D intra = 0 and Dintra = Dinter, as expected.**38**Chen, L.; Kang, Q.; Robinson, B. A.; He, Y.-L.; Tao, W.-Q. Pore-Scale Modeling of Multiphase Reactive Transport with Phase Transitions and Dissolution-Precipitation Processes in Closed Systems.*Phys. Rev. E: Stat., Nonlinear, Soft Matter Phys.*2013,*87*, 43306, DOI: 10.1103/physreve.87.04330638https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXovVahurY%253D&md5=04988a9a839ae47e240c053432561673Pore-scale modeling of multiphase reactive transport with phase transitions and dissolution-precipitation processes in closed systemsChen, Li; Kang, Qinjun; Robinson, Bruce A.; He, Ya-Ling; Tao, Wen-QuanPhysical Review E: Statistical, Nonlinear, and Soft Matter Physics (2013), 87 (4-B), 043306/1-043306/16CODEN: PRESCM; ISSN:1539-3755. (American Physical Society)A pore-scale model based on the Lattice Boltzmann (LB) method is developed for multiphase reactive transport with phase transitions and dissoln.-pptn. processes. The model combines the single-component multiphase Shan-Chen LB model, the mass transport LB model, and the dissoln.-pptn. model. Care is taken to handle information on computational nodes undergoing solid-liq. or liq.-vapor phase changes to guarantee mass and momentum conservation. A general LB concn. boundary condition is proposed that can handle various concn. boundaries including reactive and moving boundaries with complex geometries. The pore-scale model can capture coupled nonlinear multiple physicochem. processes including multiphase flow with phase sepns., mass transport, chem. reactions, dissoln.-pptn. processes, and dynamic evolution of the pore geometries. The model is validated using several multiphase flow and reactive transport problems and then used to study the thermal migration of a brine inclusion in a salt crystal. Multiphase reactive transport phenomena with phase transitions between liq.-vapor phases and dissoln.-pptn. processes of the salt in the closed inclusion are simulated and the effects of the initial inclusion size and temp. gradient on the thermal migration are investigated.**39**Kang, Q.; Zhang, D.; Lichtner, P. C.; Tsimpanogiannis, I. N. Lattice Boltzmann Model for Crystal Growth from Supersaturated Solution.*Geophys. Res. Lett.*2004,*31*, GL021107, DOI: 10.1029/2004gl021107There is no corresponding record for this reference.**40**Chen, L.; Kang, Q.; Carey, B.; Tao, W. Q. Pore-Scale Study of Diffusion-Reaction Processes Involving Dissolution and Precipitation Using the Lattice Boltzmann Method.*Int. J. Heat Mass Transfer*2014,*75*, 483– 496, DOI: 10.1016/j.ijheatmasstransfer.2014.03.074There is no corresponding record for this reference.**41**Kang, Q.; Chen, L.; Valocchi, A. J.; Viswanathan, H. S. Pore-Scale Study of Dissolution-Induced Changes in Permeability and Porosity of Porous Media.*J. Hydrol.*2014,*517*, 1049– 1055, DOI: 10.1016/j.jhydrol.2014.06.045There is no corresponding record for this reference.**42**Chen, L.; Kang, Q.; Viswanathan, H. S.; Tao, W.-Q. Pore-scale study of dissolution-induced changes in hydrologic properties of rocks with binary minerals.*Water Resour. Res.*2014,*50*, 9343– 9365, DOI: 10.1002/2014wr015646There is no corresponding record for this reference.**43**Parkhurst, B. D. L.*User’s Guide to PHREEQC ─ a Computer Program for Inverse Geochemical Calculations*; U.S. Geological Survey, 1995.There is no corresponding record for this reference.**44**Charlton, S. R.; Parkhurst, D. L. Modules Based on the Geochemical Model PHREEQC for Use in Scripting and Programming Languages.*Comput. Geosci.*2011,*37*, 1653– 1663, DOI: 10.1016/j.cageo.2011.02.00544https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhtFGnsLrK&md5=b09d42ff22fe7e8dced6821edeb0d26aModules based on the geochemical model PHREEQC for use in scripting and programming languagesCharlton, Scott R.; Parkhurst, David L.Computers & Geosciences (2011), 37 (10), 1653-1663CODEN: CGEODT; ISSN:0098-3004. (Elsevier Ltd.)The geochem. model PHREEQC is capable of simulating a wide range of equil. reactions between water and minerals, ion exchangers, surface complexes, solid solns., and gases. It also has a general kinetic formulation that allows modeling of nonequil. mineral dissoln. and pptn., microbial reactions, decompn. of org. compds., and other kinetic reactions. To facilitate use of these reaction capabilities in scripting languages and other models, PHREEQC has been implemented in modules that easily interface with other software. A Microsoft COM (component object model) has been implemented, which allows PHREEQC to be used by any software that can interface with a COM server-for example, Excel, Visual Basic, Python, or MATLAB. PHREEQC has been converted to a C++ class, which can be included in programs written in C++. The class also has been compiled in libraries for Linux and Windows that allow PHREEQC to be called from C++, C, and Fortran. A limited set of methods implements the full reaction capabilities of PHREEQC for each module. Input methods use strings or files to define reaction calcns. in exactly the same formats used by PHREEQC. Output methods provide a table of user-selected model results, such as concns., activities, satn. indexes, and densities. The PHREEQC module can add geochem. reaction capabilities to surface-water, groundwater, and watershed transport models. It is possible to store and manipulate soln. compns. and reaction information for many cells within the module. In addn., the object-oriented nature of the PHREEQC modules simplifies implementation of parallel processing for reactive-transport models. The PHREEQC COM module may be used in scripting languages to fit parameters; to plot PHREEQC results for field, lab., or theor. investigations; or to develop new models that include simple or complex geochem. calcns.**45**Parkhurst, D. L.; Wissmeier, L. PhreeqcRM: A Reaction Module for Transport Simulators Based on the Geochemical Model PHREEQC.*Adv. Water Resour.*2015,*83*, 176– 189, DOI: 10.1016/j.advwatres.2015.06.00145https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVegtb7L&md5=06eb364cf39edb534432e811c5f5fb2aPhreeqcRM: A reaction module for transport simulators based on the geochemical model PHREEQCParkhurst, David L.; Wissmeier, LaurinAdvances in Water Resources (2015), 83 (), 176-189CODEN: AWREDI; ISSN:0309-1708. (Elsevier Ltd.)PhreeqcRM is a geochem. reaction module designed specifically to perform equil. and kinetic reaction calcns. for reactive transport simulators that use an operator-splitting approach. The basic function of the reaction module is to take component concns. from the model cells of the transport simulator, run geochem. reactions, and return updated component concns. to the transport simulator. If multicomponent diffusion is modeled (e.g., Nernst-Planck equation), then aq. species concns. can be used instead of component concns. The reaction capabilities are a complete implementation of the reaction capabilities of PHREEQC. In each cell, the reaction module maintains the compn. of all of the reactants, which may include minerals, exchangers, surface complexers, gas phases, solid solns., and user-defined kinetic reactants.PhreeqcRM assigns initial and boundary conditions for model cells based on std. PHREEQC input definitions (files or strings) of chem. compns. of solns. and reactants. Addnl. PhreeqcRM capabilities include methods to eliminate reaction calcns. for inactive parts of a model domain, transfer concns. and other model properties, and retrieve selected results. The module demonstrates good scalability for parallel processing by using multiprocessing with MPI (message passing interface) on distributed memory systems, and limited scalability using multithreading with OpenMP on shared memory systems. PhreeqcRM is written in C++, but interfaces allow methods to be called from C or Fortran. By using the PhreeqcRM reaction module, an existing multicomponent transport simulator can be extended to simulate a wide range of geochem. reactions. Results of the implementation of PhreeqcRM as the reaction engine for transport simulators PHAST and FEFLOW are shown by using an anal. soln. and the reactive transport benchmark of MoMaS.**46**Liu, S.; Zhang, C.; Ghahfarokhi, R. B. A Review of Lattice-Boltzmann Models Coupled with Geochemical Modeling Applied for Simulation of Advanced Waterflooding and Enhanced Oil Recovery Processes.*Energy Fuels*2021,*35*, 13535– 13549, DOI: 10.1021/acs.energyfuels.1c0134746https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXhvVegs73J&md5=f07540711341a48270d91ac342cac3d4A Review of Lattice-Boltzmann Models Coupled with Geochemical Modeling Applied for Simulation of Advanced Waterflooding and Enhanced Oil Recovery ProcessesLiu, Siyan; Zhang, Chi; Ghahfarokhi, Reza BaratiEnergy & Fuels (2021), 35 (17), 13535-13549CODEN: ENFUEM; ISSN:0887-0624. (American Chemical Society)A review. To maintain economic profit and improve the oil prodn. efficiency after the primary and secondary prodn. phase, advanced waterflooding techniques such as low salinity waterflooding in carbonate reservoirs have been investigated in numerical simulations, lab. expts., and field pilot tests. Multiple underlying mechanisms have been proposed based on these studies, and they are still under debate. Various numerical modeling approaches are introduced, but there exists a lack of a pore-scale comprehensive modeling scheme to fully understand the processes. Lattice-Boltzmann method (LBM) is a type of numerical fluid flow modeling technique that shows capabilities and flexibilities in modeling pore-scale fluid flow to integrate phys.-chem. processes within complex structures. The intrinsic feature of LBM makes it a promising framework for simulating advanced waterflooding due to its flexibility, accuracy, and parallel efficiency. LBM works either by itself for solving reactive transport problems or by coupling with a third-party reaction solver. This review mainly introduces the LBM fluid flow and reactive transport capabilities and the concept and modeling approaches to simulate advanced waterflooding techniques. Meanwhile, an evaluation of the coupled LBM models for enhanced oil recovery (EOR) simulations is discussed with future research challenges and directions concluded.**47**Kazemi Nia Korrani, A.; Jerauld, G. R.; Sepehrnoori, K. Coupled Geochemical-Based Modeling of Low Salinity Waterflooding.*SPE Improved Oil Recovery Symposium*, 2014, No. 2008; Vol. 1–23.There is no corresponding record for this reference.**48**Patel, R.; Perko, J.; Jacques, D.; De Schutter, G.; Ye, G.; Van Breugel, K. Lattice Boltzmann Based Multicomponent Reactive Transport Model Coupled with Geochemical Solver for Scale Simulations.*Computational Methods for Coupled Problems in Science and Engineering*, 2013; pp 806– 817.There is no corresponding record for this reference.**49**Patel, R. A.; Perko, J.; Jacques, D.; De Schutter, G.; Van Breugel, K.; Ye, G. A Versatile Pore-Scale Multicomponent Reactive Transport Approach Based on Lattice Boltzmann Method: Application to Portlandite Dissolution.*Phys. Chem. Earth*2014,*70–71*, 127– 137, DOI: 10.1016/j.pce.2014.03.001There is no corresponding record for this reference.**50**Patel, R. A.; Perko, J.; Jacques, D.; De Schutter, G.; Ye, G.; Van Breugel, K. A Three-Dimensional Lattice Boltzmann Method Based Reactive Transport Model to Simulate Changes in Cement Paste Microstructure Due to Calcium Leaching.*Constr. Build. Mater.*2018,*166*, 158– 170, DOI: 10.1016/j.conbuildmat.2018.01.11450https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXjvFyhu7g%253D&md5=1a5b131cd868d07924d4c2b95204c3b4A three-dimensional lattice Boltzmann method based reactive transport model to simulate changes in cement paste microstructure due to calcium leachingPatel, Ravi A.; Perko, Janez; Jacques, Diederik; De Schutter, Geert; Ye, Guang; Van Breugel, KlaasConstruction and Building Materials (2018), 166 (), 158-170CODEN: CBUMEZ; ISSN:1879-0526. (Elsevier Ltd.)In this paper, a newly developed lattice Boltzmann method based reactive transport model to simulate changes in microstructure of ordinary Portland cement paste due to calcium leaching is presented. The model takes three-dimensional digitized cement paste microstructure as input and is capable to capture an evolution of microstructure due to leaching, accounting for the dissoln. of portlandite and corresponding increase in capillary porosity and the decalcification of C-S-H resulting in increase in gel porosity. The developed model has been applied to microstructures generated using two cement hydration models, CEMHYD3D and HYMSOTRUC, for three water-to-cement ratios. It was obsd. that the rate of leaching is directly proportional to ability of microstructure to transport calcium ions and higher fraction of percolated capillary pores result in higher rate of leaching. The model qual. reproduces exptl. obsd. changes in cement paste porosity and pore size distribution due to leaching. The quant. validation of model at this scale is not possible by comparison of leaching obtained expts. and simulations which can be attributed to several factors including the differences in the scales of expt. and modeling study presented in this paper.**51**Fazeli, H.; Patel, R.; Hellevang, H. Effect of Pore-Scale Mineral Spatial Heterogeneity on Chemically Induced Alterations of Fractured Rock: A Lattice Boltzmann Study.*Geofluids*2018,*2018*, 1– 28, DOI: 10.1155/2018/6046182There is no corresponding record for this reference.**52**Fazeli, H.; Patel, R. A.; Ellis, B. R.; Hellevang, H. Three-Dimensional Pore-Scale Modeling of Fracture Evolution in Heterogeneous Carbonate Caprock Subjected to CO2-Enriched Brine.*Environ. Sci. Technol.*2019,*53*, 4630– 4639, DOI: 10.1021/acs.est.8b0565352https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXmsFCrt7c%253D&md5=0da9cda915a6a9a1d9560c3ea85e0f03Three-Dimensional Pore-Scale Modeling of Fracture Evolution in Heterogeneous Carbonate Caprock Subjected to CO2-Enriched BrineFazeli, Hossein; Patel, Ravi A.; Ellis, Brian R.; Hellevang, HelgeEnvironmental Science & Technology (2019), 53 (8), 4630-4639CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)Fractures in caprocks overlying CO2 storage reservoirs can adversely affect the sealing capacity of the rocks. Interactions between acidified fluid and minerals with different reactivities along a fracture pathway can affect the chem. induced changes in hydrodynamic properties of fractures. To study porosity and permeability evolution of small-scale (millimeter scale) fractures, a three-dimensional pore-scale reactive transport model based on the lattice Boltzmann method has been developed. The model simulates the evolution of two different fractured carbonate-rich caprock samples subjected to a flow of CO2-rich brine. The results show that the existence of nonreactive minerals along the flow path can restrict the increase in permeability and the cubic law used to relate porosity and permeability in monomineral fractured systems is therefore not valid in multimineral systems. Moreover, the injection of CO2-acidified brine at high rates resulted in a more permeable fractured media in comparison to the case with lower injection rates. The overall rate of calcite dissoln. along the fracture decreased over time, confirming similar observations from previous continuum scale models. The presented 3D pore-scale model can be used to provide inputs for continuum scale models, such as improved porosity-permeability relationships for heterogeneous rocks, and also to investigate other reactive transport processes in the context of CO2 leakage in fractured seals.**53**Fazeli, H.; Masoudi, M.; Patel, R. A.; Aagaard, P.; Hellevang, H. Pore-Scale Modeling of Nucleation and Growth in Porous Media.*ACS Earth Space Chem.*2020,*4*, 249– 260, DOI: 10.1021/acsearthspacechem.9b0029053https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmsFCktQ%253D%253D&md5=717098fb63bf7cb682d66011a6b6c562Pore-Scale Modeling of Nucleation and Growth in Porous MediaFazeli, Hossein; Masoudi, Mohammad; Patel, Ravi A.; Aagaard, Per; Hellevang, HelgeACS Earth and Space Chemistry (2020), 4 (2), 249-260CODEN: AESCCQ; ISSN:2472-3452. (American Chemical Society)During the chem. interactions between fluid and minerals in different geol. processes, it is of high importance to predict where secondary ppts. form in the porous rocks as it helps correctly predict the hydrodynamic properties of the porous media. The reactive transport models developed for this purpose need to account for the nucleation process which is probabilistic by nature. To knowledge, the probabilistic nature of nucleation based on the classical nucleation theory was not accounted for previously in reactive transport models. The authors develop a new probabilistic nucleation model and incorporate it into a pore-scale reactive transport solver to simulate the mineral nucleation and growth in the porous media. Simulations are performed for different supersaturations, growth rates, and flow rates using a single-component mineral reaction. Simulations show that initial supersaturations strongly affect the pattern of secondary ppt. formation. Higher initial supersaturations lead to more uniformly dispersed nucleation on all the grains, while the lower initial supersaturations result in more isolated patterns. Decreasing the growth rate favors the formation of uniformly dispersed nuclei, whereas higher growth rates cause more isolated nucleation. Injection of fluid with a higher velocity gives rise to more pptn. Also, comparison of probabilistic and deterministic nucleation showed that the isolated nucleation patterns cannot be modeled using the deterministic approach. Permeability for the porous media is influenced by the pattern of secondary ppt. growth and generally, the permeability has a direct relation with the initial supersatn. and an inverse relation with the growth rate and the flow rate. Finally, the model was applied for simulation of calcite nucleation and growth on quartz grains. The calcite nucleation and growth exhibit similar behavior to those obsd. for single-species simulations.**54**Kazemi Nia Korrani, A.; Sepehrnoori, K.; Delshad, M. Coupling IPhreeqc with UTCHEM to Model Reactive Flow and Transport.*Comput. Geosci.*2015,*82*, 152– 169, DOI: 10.1016/j.cageo.2015.06.00454https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVOktbnL&md5=ce9c82dd34f3b4df4684063739df8066Coupling IPhreeqc with UTCHEM to model reactive flow and transportKazemi Nia Korrani, Aboulghasem; Sepehrnoori, Kamy; Delshad, MojdehComputers & Geosciences (2015), 82 (), 152-169CODEN: CGEODT; ISSN:0098-3004. (Elsevier Ltd.)A detailed step-by-step algorithm is presented through which we integrate IPhreeqc of the United Stated Geol. Survey (USGS) state-of-the-art geochem. package with UTCHEM for comprehensive reactive-transport modeling. UTCHEM is 3D multi-phase flow and transport research simulator developed in The University of Texas at Austin. On the other hand, IPhreeqc is the open-source modules of the USGS state-of-the-art geochem. package, PHREEQC. Through this coupling, we are able to simulate homogeneous and heterogeneous, irreversible, and ion-exchange and surface reactions under non-isothermal, non-isobaric and both local-equil. and kinetic conditions. All the data communications between UTCHEM and IPhreeqc is performed through the computer memory without writing/reading files. We further parallelize the geochem. module of UTCHEM-IPhreeqc in order to conduct field scale reservoir simulations. Our proposed coupling procedure can be implemented in any existing reservoir simulator for comprehensive reactive-transport modeling. One realistic case study is presented using UTCHEM-IPhreeqc.**55**Nardi, A.; Idiart, A.; Trinchero, P.; de Vries, L. M.; Molinero, J. Interface COMSOL-PHREEQC (ICP), an Efficient Numerical Framework for the Solution of Coupled Multiphysics and Geochemistry.*Comput. Geosci.*2014,*69*, 10– 21, DOI: 10.1016/j.cageo.2014.04.01155https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtVCjtrvI&md5=045348b6b7ed9426eeb1273874dfbf35Interface COMSOL-PHREEQC (iCP), an efficient numerical framework for the solution of coupled multiphysics and geochemistryNardi, Albert; Idiart, Andres; Trinchero, Paolo; de Vries, Luis Manuel; Molinero, JorgeComputers & Geosciences (2014), 69 (), 10-21CODEN: CGEODT; ISSN:0098-3004. (Elsevier Ltd.)This paper presents the development, verification and application of an efficient interface, denoted as iCP, which couples two standalone simulation programs: the general purpose Finite Element framework COMSOL Multiphysics and the geochem. simulator PHREEQC. The main goal of the interface is to maximize the synergies between the aforementioned codes, providing a numerical platform that can efficiently simulate a wide no. of multiphysics problems coupled with geochem. iCP is written in Java and uses the IPhreeqc C++ dynamic library and the COMSOL Java-API. Given the large computational requirements of the aforementioned coupled models, special emphasis has been placed on numerical robustness and efficiency. To this end, the geochem. reactions are solved in parallel by balancing the computational load over multiple threads. First, a benchmark exercise is used to test the reliability of iCP regarding flow and reactive transport. Then, a large scale thermo-hydro-chem. (THC) problem is solved to show the code capabilities. The results of the verification exercise are successfully compared with those obtained using PHREEQC and the application case demonstrates the scalability of a large scale model, at least up to 32 threads.**56**Muniruzzaman, M.; Rolle, M. Modeling Multicomponent Ionic Transport in Groundwater with IPhreeqc Coupling: Electrostatic Interactions and Geochemical Reactions in Homogeneous and Heterogeneous Domains.*Adv. Water Resour.*2016,*98*, 1– 15, DOI: 10.1016/j.advwatres.2016.10.01356https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xhslelsr%252FM&md5=22ba899e1ea15f8fa04941672dab91d4Modeling multicomponent ionic transport in groundwater with IPhreeqc coupling: Electrostatic interactions and geochemical reactions in homogeneous and heterogeneous domainsMuniruzzaman, Muhammad; Rolle, MassimoAdvances in Water Resources (2016), 98 (), 1-15CODEN: AWREDI; ISSN:0309-1708. (Elsevier Ltd.)The key role of small-scale processes like mol. diffusion and electrochem. migration has been increasingly recognized in multicomponent reactive transport in satd. porous media. In this study, we propose a two-dimensional multicomponent reactive transport model taking into account the electrostatic interactions during transport of charged ions in phys. and chem. heterogeneous porous media. The modeling approach is based on the local charge balance and on the description of compd.-specific and spatially variable diffusive/dispersive fluxes. The multicomponent ionic transport code is coupled with the geochem. code PHREEQC-3 by utilizing the IPhreeqc module, thus enabling to perform the geochem. calcns. included in the PHREEQC's reaction package. The multicomponent reactive transport code is benchmarked with different 1-D and 2-D transport problems. Successively, conservative and reactive transport examples are presented to demonstrate the capability of the proposed model to simulate transport of charged species in heterogeneous porous media with spatially variable phys. and chem. properties. The results reveal that the Coulombic cross-coupling between dispersive fluxes can significantly influence conservative as well as reactive transport of charged species both at the lab. and at the field scale.**57**Parkhurst, D. L.; Kipp, K. L.; Engesgaard, P.; Charlton, S. R.*PHAST─A Program for Simulating Ground-Water Flow, Solute Transport, and Multicomponent Geochemical Reactions*, U.S. Geological Survey Techniques and Methods 6-A8; U.S. Geological Survey, 2004.There is no corresponding record for this reference.**58**Parkhurst, D.; Kipp, K.; Charlton, S.*PHAST Version 2 - A Program for Simulating Groundwater Flow, Solute Transport, and Multicomponent Geochemical Reactions*, Modeling Techniques, Book 6; U.S. Geological Survey, 2010.There is no corresponding record for this reference.**59**Diersch, H. J. G.*FEFLOW: Finite Element Modeling of Flow, Mass and Heat Transport in Porous and Fractured Media*; Springer Science & Business Media, 2014.There is no corresponding record for this reference.**60**Muniruzzaman, M.; Rolle, M. Multicomponent Ionic Transport Modeling in Physically and Electrostatically Heterogeneous Porous Media With PhreeqcRM Coupling for Geochemical Reactions.*Water Resour. Res.*2019,*55*, 11121– 11143, DOI: 10.1029/2019wr02637360https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhslaqurk%253D&md5=2c29e79882b7a2ff2d71d1f21d954078Multicomponent Ionic Transport Modeling in Physically and Electrostatically Heterogeneous Porous Media With PhreeqcRM Coupling for Geochemical ReactionsMuniruzzaman, Muhammad; Rolle, MassimoWater Resources Research (2019), 55 (12), 11121-11143CODEN: WRERAQ; ISSN:0043-1397. (Wiley-Blackwell)Low-permeability aquitards, such as clay layers and inclusions, are of utmost importance for contaminant transport in groundwater systems. Although most dissolved species, contaminants, and clay surfaces are charged, the role of electrostatic interactions in subsurface flow-through systems has not been extensively investigated. This study presents a two-dimensional multicomponent reactive transport investigation of diffusive/dispersive and electrostatic processes in homogeneous and heterogeneous clay systems. The proposed approach is based on multiple continua and is capable to accurately describe charge interactions during ionic transport in the free water, diffuse layer, and interlayer water of charged porous media. The diffuse layer compn. is simulated by considering a mean electrostatic potential following Donnan approach, whereas the interlayer compn. is calcd. by adopting the Gaines-Thomas convention. Diffusive/dispersive fluxes within each subcontinuum (free water, diffuse layer, and interlayer) are calcd. solving the Nernst-Planck equation while maintaining a net zero-charge flux. Furthermore, the multidimensional flow and transport model is coupled with the geochem. code PHREEQC, by utilizing the PhreeqcRM module, thus enabling great flexibility to access all PHREEQC's reaction capabilities. The code is benchmarked in 1-D systems against other software and previously published exptl. data. Successively, reactive transport simulations are performed in 2-D clayey-sandy flow-through domains with spatially variable phys. and electrostatic properties at both lab. and field scales. The results reveal that different properties of surface charge, diffuse layer, and Coulombic interactions impact the transport of charged species and lead to distinct spatial distribution of the ions in the different subcontinua and to significantly different breakthrough curves.**61**Fokina, D.; Muravleva, E.; Ovchinnikov, G.; Oseledets, I. Microstructure Synthesis Using Style-Based Generative Adversarial Networks.*Phys. Rev. E*2020,*101*, 43308, DOI: 10.1103/physreve.101.04330861https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtl2ms73N&md5=858978194b72c33fb84bc101338775e3Microstructure synthesis using style-based generative adversarial networksFokina, Daria; Muravleva, Ekaterina; Ovchinnikov, George; Oseledets, IvanPhysical Review E (2020), 101 (4), 043308CODEN: PREHBM; ISSN:2470-0053. (American Physical Society)A review. This work considers the usage of StyleGAN architecture for the task of microstructure synthesis. The task is the following: Given no. of samples of structure we try to generate similar samples at the same time preserving its properties. Since the considered architecture is not able to produce samples of sizes larger than the training images, we propose to use image quilting to merge fixed-sized samples. One of the key features of the considered architecture is that it uses multiple image resolns. We also investigate the necessity of such an approach.**62**Mosser, L.; Dubrule, O.; Blunt, M. J. Reconstruction of Three-Dimensional Porous Media Using Generative Adversarial Neural Networks.*Phys. Rev. E*2017,*96*, 43309, DOI: 10.1103/physreve.96.04330962https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXpslGmsQ%253D%253D&md5=7a7a3238a099fba7254c2406357dc5cfReconstruction of three-dimensional porous media using generative adversarial neural networksMosser, Lukas; Dubrule, Olivier; Blunt, Martin J.Physical Review E (2017), 96 (4), 043309/1-043309/17CODEN: PREHBM; ISSN:2470-0053. (American Physical Society)To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a no. of representative samples of the void-solid structure. While modern x-ray computer tomog. has made it possible to ext. three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often exptl. not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets.We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics.We successfully compare measures of pore morphol., such as the Euler characteristic, two-point statistics, and directional single-phase permeability of synthetic realizations with the calcd. properties of a bead pack, Berea sandstone, andKetton limestone. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.**63**Mosser, L.; Dubrule, O.; Blunt, M. J. Stochastic Reconstruction of an Oolitic Limestone by Generative Adversarial Networks.*Transp. Porous Media*2018,*125*, 81– 103, DOI: 10.1007/s11242-018-1039-963https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXmslykt74%253D&md5=12ad2967d601f7cc1209ac565365e00eStochastic Reconstruction of an Oolitic Limestone by Generative Adversarial NetworksMosser, Lukas; Dubrule, Olivier; Blunt, Martin J.Transport in Porous Media (2018), 125 (1), 81-103CODEN: TPMEEI; ISSN:0169-3913. (Springer)Stochastic image reconstruction is a key part of modern digital rock physics and material anal. that aims to create representative samples of microstructures for upsampling, upscaling and uncertainty quantification. We present new results of a method of three-dimensional stochastic image reconstruction based on generative adversarial neural networks (GANs). GANs are a family of unsupervised learning methods that require no a priori inference of the probability distribution assocd. with the training data. Thanks to the use of two convolutional neural networks, the discriminator and the generator, in the training phase, and only the generator in the simulation phase, GANs allow the sampling of large and realistic volumetric images. We apply a GAN-based workflow of training and image generation to an oolitic Ketton limestone micro-CT unsegmented gray-level dataset. Minkowski functionals calcd. as a function of the segmentation threshold are compared between simulated and acquired images. Flow simulations are run on the segmented images, and effective permeability and velocity distributions of simulated flow are also compared. Results show that GANs allow a fast and accurate reconstruction of the evaluated image dataset. We discuss the performance of GANs in relation to other simulation techniques and stress the benefits resulting from the use of convolutional neural networks . We address a no. of challenges involved in GANs, in particular the representation of the probability distribution assocd. with the training data.**64**Feng, J.; He, X.; Teng, Q.; Ren, C.; Chen, H.; Li, Y. Reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Networks.*Phys. Rev. E*2019,*100*, 33308, DOI: 10.1103/physreve.100.03330864https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXkvFyksg%253D%253D&md5=198c2a1727c52550620f6818b95c72d9Reconstruction of porous media from extremely limited information using conditional generative adversarial networksFeng, Junxi; He, Xiaohai; Teng, Qizhi; Ren, Chao; Chen, Honggang; Li, YangPhysical Review E (2019), 100 (3), 033308CODEN: PREHBM; ISSN:2470-0053. (American Physical Society)Porous media are ubiquitous in both nature and engineering applications. Therefore, their modeling and understanding is of vital importance. In contrast to direct acquisition of three-dimensional (3D) images of this type of medium, obtaining its subregion (s) such as 2D images or several small areas can be feasible. Therefore, reconstructing whole images from limited information is a primary technique in these types of cases. Given data in practice cannot generally be detd. by users and may be incomplete or only partially informed, thus making existing reconstruction methods inaccurate or even ineffective. In particular, conditional generative adversarial network is utilized to learn the mapping between the input (a partial image) and output (a full image). To ensure the reconstruction accuracy, two simple but effective objective functions are proposed and then coupled with the other two functions to jointly constrain the training procedure. Our method is extensively tested on a variety of porous materials and validated by both visual inspection and quant. comparison. It is shown to be accurate, stable, and even fast (0.08 s for a 128×128 image reconstruction). The proposed approach can be readily extended by, for example, incorporating user-defined conditional data and an arbitrary no. of object functions into reconstruction, while being coupled with other reconstruction methods.**65**Liu, S.; Zhong, Z.; Takbiri-Borujeni, A.; Kazemi, M.; Fu, Q.; Yang, Y. A Case Study on Homogeneous and Heterogeneous Reservoir Porous Media Reconstruction by Using Generative Adversarial Networks.*Energy Procedia*2019,*158*, 6164, DOI: 10.1016/j.egypro.2019.01.493There is no corresponding record for this reference.**66**Shams, R.; Masihi, M.; Boozarjomehry, R. B.; Blunt, M. J. Coupled Generative Adversarial and Auto-Encoder Neural Networks to Reconstruct Three-Dimensional Multi-Scale Porous Media.*J. Pet. Sci. Eng.*2020,*186*, 106794, DOI: 10.1016/j.petrol.2019.10679466https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXisVKrsbvE&md5=c0dee6d62fd2c986ad0eb87612878a66Coupled generative adversarial and auto-encoder neural networks to reconstruct three-dimensional multi-scale porous mediaShams, Reza; Masihi, Mohsen; Boozarjomehry, Ramin Bozorgmehry; Blunt, Martin J.Journal of Petroleum Science & Engineering (2020), 186 (), 106794CODEN: JPSEE6; ISSN:0920-4105. (Elsevier B.V.)In this study, coupled Generative Adversarial and Auto-Encoder neural networks have been used to reconstruct realizations of three-dimensional porous media. The gradient-descent-based optimization method is used for training and stabilizing the neural networks. The multi-scale reconstruction has been conducted for both sandstone and carbonate samples from an Iranian oilfield. The sandstone contains inter and intra-grain porosity. The generative adversarial network predicts the inter-grain pores and the auto-encoder provides the generative adversarial network result with intra-grain pores (micro-porosity). Different matching criteria, including porosity, permeability, auto-correlation function, and visual interpretation have been used to investigate the performance of the models. This methodol. provides researchers with a reliable method to reconstruct multi-scale realizations of porous media.**67**Varfolomeev, I.; Yakimchuk, I.; Safonov, I. An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples.*Computers*2019,*8*, 72, DOI: 10.3390/computers8040072There is no corresponding record for this reference.**68**Niu, Y.; Mostaghimi, P.; Shabaninejad, M.; Swietojanski, P.; Armstrong, R. T. Digital Rock Segmentation for Petrophysical Analysis With Reduced User Bias Using Convolutional Neural Networks.*Water Resour. Res.*2020,*56*, e2019WR026597 DOI: 10.1029/2019wr026597There is no corresponding record for this reference.**69**Karimpouli, S.; Tahmasebi, P. Image-Based Velocity Estimation of Rock Using Convolutional Neural Networks.*Neural Networks*2019,*111*, 89– 97, DOI: 10.1016/j.neunet.2018.12.00669https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3cjmtFWltg%253D%253D&md5=374b0da504ea8887ff9ce06f0bdb7c3aImage-based velocity estimation of rock using Convolutional Neural NetworksKarimpouli Sadegh; Tahmasebi PejmanNeural networks : the official journal of the International Neural Network Society (2019), 111 (), 89-97 ISSN:.Digital images of rock samples have been using extensively in Digital Rock Physics (DRP) to evaluate physical parameters of rock such as permeability, P- and S-wave velocities and formation factor. The parameters are numerically computed by simulation of the corresponding physical processes through segmented image of rock, which provide a direct and accurate evaluation of rock properties. However, recent advances in machine learning and Convolutional Neural Networks (CNN) allow using images as input. Such networks, however, require a considerable number of images as input. In this paper, CNNs are used to estimate the P- and S-wave velocities from images of rock medium. To deal with lack of input data, a hybrid pattern- and pixel-based simulation (HYPPS) is used as an efficient data augmentation method to increase the training data set. For each input image, 10 stochastic realizations are produced. Compare to the case wherein the stochastic models are not used, the new results from the enhanced network indicate a sharp improvement in the estimations such that R(2) is increased to 0.94. Furthermore, the newly developed CNN network, unlike the one with the small data set (R(2)=0.75), manifests no over/underestimation. The estimated properties, in comparison with the computational results, indicate that CNNs perform outstandingly in predicting the physical parameters of rock without conducting any time-demanding forward modeling if enough input data are provided.**70**Wang, Y. D.; Shabaninejad, M.; Armstrong, R. T.; Mostaghimi, P. Deep Neural Networks for Improving Physical Accuracy of 2D and 3D Multi-Mineral Segmentation of Rock Micro-CT Images.*Appl. Soft Comput.*2021,*104*, 107185, DOI: 10.1016/j.asoc.2021.107185There is no corresponding record for this reference.**71**Kamrava, S.; Tahmasebi, P.; Sahimi, M. Enhancing Images of Shale Formations by a Hybrid Stochastic and Deep Learning Algorithm.*Neural Networks*2019,*118*, 310– 320, DOI: 10.1016/j.neunet.2019.07.00971https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3MzpsVensQ%253D%253D&md5=dc11a632302781074ebbd56c10076c7fEnhancing images of shale formations by a hybrid stochastic and deep learning algorithmKamrava Serveh; Tahmasebi Pejman; Sahimi MuhammadNeural networks : the official journal of the International Neural Network Society (2019), 118 (), 310-320 ISSN:.Accounting for the morphology of shale formations, which represent highly heterogeneous porous media, is a difficult problem. Although two- or three-dimensional images of such formations may be obtained and analyzed, they either do not capture the nanoscale features of the porous media, or they are too small to be an accurate representative of the media, or both. Increasing the resolution of such images is also costly. While high-resolution images may be used to train a deep-learning network in order to increase the quality of low-resolution images, an important obstacle is the lack of a large number of images for the training, as the accuracy of the network's predictions depends on the extent of the training data. Generating a large number of high-resolution images by experimental means is, however, very time consuming and costly, hence limiting the application of deep-learning algorithms to such an important class of problems. To address the issue we propose a novel hybrid algorithm by which a stochastic reconstruction method is used to generate a large number of plausible images of a shale formation, using very few input images at very low cost, and then train a deep-learning convolutional network by the stochastic realizations. We refer to the method as hybrid stochastic deep-learning (HSDL) algorithm. The results indicate promising improvement in the quality of the images, the accuracy of which is confirmed by visual, as well as quantitative comparison between several of their statistical properties. The results are also compared with those obtained by the regular deep learning algorithm without using an enriched and large dataset for training, as well as with those generated by bicubic interpolation.**72**Kamrava, S.; Tahmasebi, P.; Sahimi, M. Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning.*Transp. Porous Media*2020,*131*, 427– 448, DOI: 10.1007/s11242-019-01352-5There is no corresponding record for this reference.**73**Tembely, M.; AlSumaiti, A.*Deep Learning for a Fast and Accurate Prediction of Complex Carbonate Rock Permeability From 3D Micro-CT Images*; Abu Dhabi International Petroleum Exhibition and Conference, 2019.There is no corresponding record for this reference.**74**Wu, J.; Yin, X.; Xiao, H. Seeing Permeability from Images: Fast Prediction with Convolutional Neural Networks.*Sci. Bull.*2018,*63*, 1215– 1222, DOI: 10.1016/j.scib.2018.08.00674https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB28fgtVCgsw%253D%253D&md5=045f16bb8df4840e385966fecd384dbfSeeing permeability from images: fast prediction with convolutional neural networksWu Jinlong; Yin Xiaolong; Xiao HengScience bulletin (2018), 63 (18), 1215-1222 ISSN:.Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples, (2) computation of permeability via fluid dynamics simulations, (3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially for those with dilated pores. Owning to such heterogeneity, the permeability cannot be estimated using the conventional Kozeny-Carman approach. Computational time was reduced by several orders of magnitude compared to fluid dynamic simulations. We found that, by including physical parameters that are known to affect permeability into the neural network, the physics-informed CNN generated better results than regular CNN. However, improvements vary with implemented heterogeneity.**75**Liu, S.; Zolfaghari, A.; Sattarin, S.; Dahaghi, A. K.; Negahban, S. Application of Neural Networks in Multiphase Flow through Porous Media: Predicting Capillary Pressure and Relative Permeability Curves.*J. Pet. Sci. Eng.*2019,*180*, 445– 455, DOI: 10.1016/j.petrol.2019.05.04175https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXltlOlu7s%253D&md5=d6fb170eb442a650ed6686dd33a3860aApplication of neural networks in multiphase flow through porous media: Predicting capillary pressure and relative permeability curvesLiu, Siyan; Zolfaghari, Arsalan; Sattarin, Shariar; Dahaghi, Amirmasoud Kalantari; Negahban, ShahinJournal of Petroleum Science & Engineering (2019), 180 (), 445-455CODEN: JPSEE6; ISSN:0920-4105. (Elsevier B.V.)Artificial Neural Networks (ANN) are trained to simulate two-phase capillary pressure and relative permeability data in bundles of capillary tubes with non-uniform arbitrary wettability conditions and cross-sectional shapes of different irregular convex polygons. All polygons with variable no. of corners are randomly generated for a given range of inscribed radii, shape, and elongation factors. To generate the data for the training of ANNs, the minimization of Helmholtz free energy and Mayer-Stowe-Princen (MS-P) method are combined to find thermodynamically consistent threshold capillary pressures for two-phase flow. These capillary pressures are then used to det. the sequence of displacements in different capillary tubes. We calc. saturations and phase conductance at each quasi steady-state condition where no more displacements can be done for a given capillary pressure. The generated two-phase capillary pressure and relative permeability curves are then used for the training of ANNs. We test different designs of ANNs to find the optimal workflow for the training and predicting of petrophys. properties related to multiphase flow. In this work, we present the results of two different neural network structures. In the first structure, we use ANN to predict threshold capillary pressures of different capillary tubes during a drainage process (i.e., oil-to-water displacements). In the second structure, we predict capillary pressure and relative permeability curves for an arbitrary bundle of capillary tubes. The first structure of ANNs simulates a fixed property for a given capillary tube, whereas the second structure simulates time-series data format (i.e., for a given bundle of capillary tubes calcd. properties vary with satn.). To do so, we have generated multi-phase flow properties for two large datasets consisting of 40,000 and 60,000 capillary tubes each. High-quality training datasets are crit. in the training of high-fidelity ANN models. These models can then learn the impact of a wide variety of pore geometries (i.e., shape factors and elongations). Addnl., feature selection and preprocessing of the input data could significantly impact ANN's predictions. The multi-layer perceptron (MLP) neural network with three hidden layers with four outputs is adequate for predicting capillary pressure and relative permeability curves during drainage. This model is approx. an order of magnitude faster than conventional direct calcns. using a desktop computer with four cores CPU. Such improvement in the speed of calcns. becomes significant when dealing with larger models, more dimensions, and/or introducing pore connectivity in 3D.**76**Liu, S.; Barati, R.; Zhang, C. Fast Estimation of Permeability in Sandstones by 3D Convolutional Neural Networks.*SEG International Exposition and Annual Meeting*, September 15, 2019; p D033S046R002.There is no corresponding record for this reference.**77**Wu, H.; Fang, W.-Z.; Kang, Q.; Tao, W.-Q.; Qiao, R. Predicting Effective Diffusivity of Porous Media from Images by Deep Learning.*Sci. Rep.*2019,*9*, 20387, DOI: 10.1038/s41598-019-56309-x77https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmtFCjtQ%253D%253D&md5=89fb59c778f726be938c908a869c3326Predicting Effective Diffusivity of Porous Media from Images by Deep LearningWu, Haiyi; Fang, Wen-Zhen; Kang, Qinjun; Tao, Wen-Quan; Qiao, RuiScientific Reports (2019), 9 (1), 20387CODEN: SRCEC3; ISSN:2045-2322. (Nature Research)We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topol., large variation of porosity (0.28-0.98), and effective diffusivity spanning more than one order of magnitude (0.1 .ltorsim. De < 1), e.g., >95% of predicted De have truncated relative error of <10% when the true De is larger than 0.2. The CNN model provides better prediction than the empirical Bruggeman equation, esp. for porous structure with small diffusivity. The relative error of CNN predictions, however, is rather high for structures with De < 0.1. To address this issue, the porosity of porous structures is encoded directly into the neural network but the performance is enhanced marginally. Further improvement, i.e., 70% of the CNN predictions for structures with true De < 0.1 have relative error <30%, is achieved by removing trapped regions and dead-end pathways using a simple algorithm. These results suggest that deep learning augmented by field knowledge can be a powerful technique for predicting the transport properties of porous media. Directions for future research of machine learning in porous media are discussed based on detailed anal. of the performance of CNN models in the present work.**78**Wei, H.; Zhao, S.; Rong, Q.; Bao, H. Predicting the Effective Thermal Conductivities of Composite Materials and Porous Media by Machine Learning Methods.*Int. J. Heat Mass Transfer*2018,*127*, 908– 916, DOI: 10.1016/j.ijheatmasstransfer.2018.08.082There is no corresponding record for this reference.**79**Zhang, Z.; Hong, Y.; Hou, B.; Zhang, Z.; Negahban, M.; Zhang, J. Accelerated Discoveries of Mechanical Properties of Graphene Using Machine Learning and High-Throughput Computation.*Carbon*2019,*148*, 115– 123, DOI: 10.1016/j.carbon.2019.03.04679https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXmtF2mt7s%253D&md5=450bc2084d9ed3846b7134f54419c824Accelerated discoveries of mechanical properties of graphene using machine learning and high-throughput computationZhang, Zesheng; Hong, Yang; Hou, Bo; Zhang, Zhongtao; Negahban, Mehrdad; Zhang, JingchaoCarbon (2019), 148 (), 115-123CODEN: CRBNAH; ISSN:0008-6223. (Elsevier Ltd.)Machine learning (ML) has been vastly used in various fields, but its application in engineering science remains in infancy. In this work, for the first time, different machine learning algorithms and artificial neural network (ANN) structures are used to predict the mech. properties of single-layer graphene under various impact factors of system temp., strain rate, vacancy defect and chirality. The predictions include fracture strain, fracture strength and Young's modulus. High throughput computation (HTC) combined with classical mol. dynamics (MD) simulation is used to generate the training dataset for the ML models. It was discovered that both temp. and vacancy defect have neg. effects on the predicted properties while strain rate has pos. correlations with the prediction results. The stochastic gradient descent (SGD) method could not properly capture the effects of the different impact factors on the mech. properties of graphene, while k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT) and ANN provided desirable prediction results. Discoveries in this work provide new perspectives on the study of mech. properties using state-of-the-art computational methods.**80**Santos, J. E.; Xu, D.; Jo, H.; Landry, C. J.; Prodanović, M.; Pyrcz, M. J. PoreFlow-Net: A 3D Convolutional Neural Network to Predict Fluid Flow through Porous Media.*Adv. Water Resour.*2020,*138*, 103539, DOI: 10.1016/j.advwatres.2020.103539There is no corresponding record for this reference.**81**Liu, M.; Kwon, B.; Kang, P. K. Machine Learning to Predict Effective Reaction Rates in 3D Porous Media from Pore Structural Features.*Sci. Rep.*2022,*12*, 5486, DOI: 10.1038/s41598-022-09495-081https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xosl2guro%253D&md5=0768add057d41b6b581a597adee73163Machine learning to predict effective reaction rates in 3D porous media from pore structural featuresLiu, Min; Kwon, Beomjin; Kang, Peter K.Scientific Reports (2022), 12 (1), 5486CODEN: SRCEC3; ISSN:2045-2322. (Nature Portfolio)Abstr.: Large discrepancies between well-mixed reaction rates and effective reactions rates estd. under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning (ML). We first perform pore-scale reactive transport simulations with fluid-solid reactions in hundreds of porous media and calc. effective reaction rates from pore-scale concn. fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in detg. effective reaction rates. Based on the importance information, we train artificial neural networks with varying no. of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are sp. surface, pore sphericity, and coordination no. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.**82**Tahmasebi, P.; Kamrava, S.; Bai, T.; Sahimi, M. Machine Learning in Geo- and Environmental Sciences: From Small to Large Scale.*Adv. Water Resour.*2020,*142*, 103619, DOI: 10.1016/j.advwatres.2020.103619There is no corresponding record for this reference.**83**Wang, Y. D.; Blunt, M. J.; Armstrong, R. T.; Mostaghimi, P. Deep Learning in Pore Scale Imaging and Modeling.*Earth Sci. Rev.*2021,*215*, 103555, DOI: 10.1016/j.earscirev.2021.103555There is no corresponding record for this reference.**84**Latt, J.; Malaspinas, O.; Kontaxakis, D.; Parmigiani, A.; Lagrava, D.; Brogi, F.; Belgacem, M. B.; Thorimbert, Y.; Leclaire, S.; Li, S.; Marson, F.; Lemus, J.; Kotsalos, C.; Conradin, R.; Coreixas, C.; Petkantchin, R.; Raynaud, F.; Beny, J.; Chopard, B. Palabos: Parallel Lattice Boltzmann Solver.*Comput. Math. Appl.*2021,*81*, 334– 350, DOI: 10.1016/j.camwa.2020.03.022There is no corresponding record for this reference.**85**Parkhurst, D. L.*User’s Guide to PHREEQC, a Computer Program for Speciation, Reaction-Path, Advective-Transport, and Inverse Geochemical Calculations*; U.S. Geological Survey, 1995.There is no corresponding record for this reference.**86**Bhatnagar, P. L.; Gross, E. P.; Krook, M. A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems.*Phys. Rev.*1954,*94*, 511– 525, DOI: 10.1103/physrev.94.51186https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaG2cXksVKhtg%253D%253D&md5=6f3a8c8f4fa1c6b7ca7af1dcc779a2c4A model for collision processes in gases. I. Small-amplitude processes in charged and neutral one-component systemsBhatnagar, P. L.; Gross, E. P.; Krook, M.Physical Review (1954), 94 (), 511-25CODEN: PHRVAO; ISSN:0031-899X.A kinetic-theory approach to collision processes in ionized and neutral gases is presented. This approach is adequate for the unified treatment of the dynamic properties of gases over a continuous range of pressures from the Knudsen limit to the high-pressure limit where the aerodynamic equations are valid. It is also possible to satisfy the correct microscopic boundary conditions. The method consists in altering the collision terms in the Boltzmann equation. The modified collision terms are constructed so that each collision conserves particle no., momentum, and energy; other characteristics such as persistence of velocities and angular dependence may be included. The technique is illustrated for a simple model involving the assumption of a collision time independent of velocity; this model is applied to the study of small amplitude oscillations of one-component ionized and neutral gases. The initial value problem for unbounded space is solved by performing a Fourier transformation on the space variables and a Laplace transformation on the time variable. For uncharged gases there results the correct adiabatic limiting law for sound-wave propagation at high pressures and, in addn., a theory of absorption and dispersion of sound for arbitrary pressures is obtained. For ionized gases the difference in the nature of the organization in the low-pressure plasma oscillations and in high-pressure sound-type oscillations is studied. Two important cases are distinguished. If the wave lengths of the oscillations are long compared to either the Debye length or the mean free path, a small change in frequency is obtained as the collision frequency varies from zero to infinity. The accompanying absorption is small; it reaches its max. value when the collision frequency equals the plasma frequency. The 2nd case refers to waves shorter than both the Debye length and the mean free path; these waves are characterized by a very heavy absorption.**87**Martys, N. N.; Douglas, J. J. Critical Properties and Phase Separation in Lattice Boltzmann Fluid Mixtures.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*2001,*63*, 031205, DOI: 10.1103/physreve.63.031205There is no corresponding record for this reference.**88**Qian, Y. H.; D’Humières, D.; Lallemand, P. Lattice Bgk Models for Navier-Stokes Equation.*EPL*1992,*17*, 479– 484, DOI: 10.1209/0295-5075/17/6/001There is no corresponding record for this reference.**89**Martys, N. S.; Chen, H. Simulation of Multicomponent Fluids in Complex Three-Dimensional Geometries by the Lattice Boltzmann Method.*Phys. Rev. E: Stat. Phys., Plasmas, Fluids, Relat. Interdiscip. Top.*1996,*53*, 743– 750, DOI: 10.1103/physreve.53.74389https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28Xnsl2itw%253D%253D&md5=d06bd1d7428e69961f672bea767dc73fSimulation of multicomponent fluids in complex three-dimensional geometries by the lattice Boltzmann methodMartys, Nicos S.; Chen, HudongPhysical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics (1996), 53 (1-B), 743-750CODEN: PLEEE8; ISSN:1063-651X. (American Physical Society)We describe an implementation of a the recently proposed lattice Boltzmann based model of Shan and Chen [Phys. Rev. E 47, 1815 (1993); 49, 2941 (1994)] sto simulate multicomponent flow in complex three-dimensional geometries such as porous media. The above method allows for the direct incorporation of fluid-fluid and fluid-solid interactiosn as well as an applied external force. As a test of this method, we obtained Poiseuille flow for the case of a single fluid driven by a const. body force and obtained results consistent with Laplace's law for the case of two immiscible fluids. The displacement of one fluid by another in a porous medium was then modeled. The relative permeability for different wetting fluid saturatios of a microtomog.-generated image of sandstone was calcd. and compared favorably with expt. In addn., we show that a first-order phase transition, in three dimensions, may be obtained by this lattice Boltzman method, demonstrating the potential for modeling phase transitions and multiphase flow in porous media.**90**Timm, K.; Halim, K.; Alexandr, K.; Orest, S.; Goncalo, S.; Erlend, M. V.*The Lattice Boltzmann Method Principles and Practice*; Springer, 2017.There is no corresponding record for this reference.**91**Huber, C.; Parmigiani, A.; Chopard, B.; Manga, M.; Bachmann, O. Lattice Boltzmann Model for Melting with Natural Convection.*Int. J. Heat Fluid Flow*2008,*29*, 1469– 1480, DOI: 10.1016/j.ijheatfluidflow.2008.05.00291https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtFCksLvN&md5=e2a1c69e82786792d4ff6ab58b2996dcLattice Boltzmann model for melting with natural convectionHuber, Christian; Parmigiani, Andrea; Chopard, Bastien; Manga, Michael; Bachmann, Olivier**92**Parmigiani, A. Lattice Boltzmann Calculations of Reactive Multiphase Flows in Porous Media, Thesis, University of Geneva, 2011; Vol. 129.There is no corresponding record for this reference.**93**Fazeli, H.; Patel, R. A.; Ellis, B. R.; Hellevang, H. Three-Dimensional Pore-Scale Modeling of Fracture Evolution in Heterogeneous Carbonate Caprock Subjected to CO2-Enriched Brine.*Environ. Sci. Technol.*2019,*53*, 4630– 4639, DOI: 10.1021/acs.est.8b0565393https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXmsFCrt7c%253D&md5=0da9cda915a6a9a1d9560c3ea85e0f03Fazeli, Hossein; Patel, Ravi A.; Ellis, Brian R.; Hellevang, Helge**94**Fazeli, H.; Masoudi, M.; Patel, R. A.; Aagaard, P.; Hellevang, H. Pore-Scale Modeling of Nucleation and Growth in Porous Media.*ACS Earth Space Chem.*2020,*4*, 249– 260, DOI: 10.1021/acsearthspacechem.9b0029094https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmsFCktQ%253D%253D&md5=717098fb63bf7cb682d66011a6b6c562Pore-Scale Modeling of Nucleation and Growth in Porous MediaFazeli, Hossein; Masoudi, Mohammad; Patel, Ravi A.; Aagaard, Per; Hellevang, Helge**95**Patel, R.; Perko, J.; Jacques, D.; de Schutter, G.; Ye, G.; van Breugel, K. Lattice Boltzmann Based Multicomponent Reactive Transport Model Coupled with Geochemical Solver for Scale Simulations.*Computational Methods for Coupled Problems in Science and Engineering*, 2013; pp 806– 817.There is no corresponding record for this reference.**96**Patel, R. A.; Perko, J.; Jacques, D.; de Schutter, G.; van Breugel, K.; Ye, G. A Versatile Pore-Scale Multicomponent Reactive Transport Approach Based on Lattice Boltzmann Method: Application to Portlandite Dissolution.*Phys. Chem. Earth*2014,*70–71*, 127– 137, DOI: 10.1016/j.pce.2014.03.001There is no corresponding record for this reference.**97**Yoon, H.; Kang, Q.; Valocchi, A. J. 12. Lattice Boltzmann-Based Approaches for Pore-Scale Reactive Transport.*Rev. Mineral. Geochem.*2015,*80*, 393– 432, DOI: 10.1515/9781501502071-012There is no corresponding record for this reference.**98**Latt, J.; Malaspinas, O.; Kontaxakis, D.; Parmigiani, A.; Lagrava, D.; Brogi, F.; Belgacem, M. B.; Thorimbert, Y.; Leclaire, S.; Li, S.; Marson, F.; Lemus, J.; Kotsalos, C.; Conradin, R.; Coreixas, C.; Petkantchin, R.; Raynaud, F.; Beny, J.; Chopard, B. Palabos: Parallel Lattice Boltzmann Solver.*Computers & Mathematics with Applications*, 2020.There is no corresponding record for this reference.**99**Tan, J.; Sinno, T. R.; Diamond, S. L. A parallel fluid-solid coupling model using LAMMPS and Palabos based on the immersed boundary method.*J. Comput. Sci.*2018,*25*, 89– 100, DOI: 10.1016/j.jocs.2018.02.00699https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3c3ptlOktw%253D%253D&md5=8161f6ee17165f61ed137ec8cc917ffcA parallel fluid-solid coupling model using LAMMPS and Palabos based on the immersed boundary methodTan Jifu; Sinno Talid; Diamond Scott LJournal of computational science (2018), 25 (), 89-100 ISSN:1877-7503.The study of viscous fluid flow coupled with rigid or deformable solids has many applications in biological and engineering problems, e.g., blood cell transport, drug delivery, and particulate flow. We developed a partitioned approach to solve this coupled Multiphysics problem. The fluid motion was solved by Palabos (Parallel Lattice Boltzmann Solver), while the solid displacement and deformation was simulated by LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator). The coupling was achieved through the immersed boundary method (IBM). The code modeled both rigid and deformable solids exposed to flow. The code was validated with the Jeffery orbits of an ellipsoid particle in shear flow, red blood cell stretching test, and effective blood viscosity flowing in tubes. It demonstrated essentially linear scaling from 512 to 8192 cores for both strong and weak scaling cases. The computing time for the coupling increased with the solid fraction. An example of the fluid-solid coupling was given for flexible filaments (drug carriers) transport in a flowing blood cell suspensions, highlighting the advantages and capabilities of the developed code.**100**Kotsalos, C.; Latt, J.; Chopard, B. Bridging the Computational Gap between Mesoscopic and Continuum Modeling of Red Blood Cells for Fully Resolved Blood Flow.*J. Comput. Phys.*2019,*398*, 108905, DOI: 10.1016/j.jcp.2019.108905There is no corresponding record for this reference.**101**Kotsalosa, C.; Latt, J.; Beny, J.; Chopard, B. Digital Blood in Massively Parallel CPU/GPU Systems for the Study of Platelet Transport.*Interface Focus*2019,*11*, 20190116, DOI: 10.1098/rsfs.2019.0116There is no corresponding record for this reference.**102**Parkhurst, D. L.; Appelo, C. A. J.*Description of Input and Examples for PHREEQC Version 3 ─ A Computer Program for Speciation, Batch-Reaction , One-Dimensional Transport , and Inverse Geochemical Calculations*, U.S. Geological Survey Techniques and Methods, Book 6, Chapter A43, 2013, 6-43A, p 497; U.S. Geological Survey, 2013There is no corresponding record for this reference.**103**Tetteh, J. T.; Alimoradi, S.; Brady, P. v.; Barati Ghahfarokhi, R. Electrokinetics at Calcite-Rich Limestone Surface: Understanding the Role of Ions in Modified Salinity Waterflooding.*J. Mol. Liq.*2020,*297*, 111868, DOI: 10.1016/j.molliq.2019.111868103https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFyrt7bO&md5=e0217208becbeafe29710c5a738d0c05Tetteh, Joel T.; Alimoradi, Sirwan; Brady, Patrick V.; Barati Ghahfarokhi, RezaJournal of Molecular Liquids (2020), 297 (), 111868CODEN: JMLIDT; ISSN:0167-7322. (Elsevier B.V.)**104**Lutzenkirchen, J.*Surface Complexation Modelling*; Elsevier, 2006.There is no corresponding record for this reference.**105**Tetteh, J. T.; Alimoradi, S.; Brady, P. V.; Barati, R. G. Electrokinetics at Calcite-Rich Limestone Surface: Understanding the Role of Ions in Modified Salinity Waterflooding.*J. Mol. Liq.*2020,*297*, 111868, DOI: 10.1016/j.molliq.2019.111868105https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFyrt7bO&md5=e0217208becbeafe29710c5a738d0c05Tetteh, Joel T.; Alimoradi, Sirwan; Brady, Patrick V.; Barati Ghahfarokhi, RezaJournal of Molecular Liquids (2020), 297 (), 111868CODEN: JMLIDT; ISSN:0167-7322. (Elsevier B.V.)**106**Tetteh, J. T.; Alimoradi, S.; Brady, P. v.; Barati, R. G. Electrokinetics at Calcite-Rich Limestone Surface: Understanding the Role of Ions in Modified Salinity Waterflooding.*J. Mol. Liq.*2020,*297*, 111868, DOI: 10.1016/j.molliq.2019.111868106https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFyrt7bO&md5=e0217208becbeafe29710c5a738d0c05Tetteh, Joel T.; Alimoradi, Sirwan; Brady, Patrick V.; Barati Ghahfarokhi, RezaJournal of Molecular Liquids (2020), 297 (), 111868CODEN: JMLIDT; ISSN:0167-7322. (Elsevier B.V.)**107**Molins, S.; Soulaine, C.; Prasianakis, N. I.; Abbasi, A.; Poncet, P.; Ladd, A. J. C.; Starchenko, V.; Roman, S.; Trebotich, D.; Tchelepi, H. A.; Steefel, C. I. Simulation of Mineral Dissolution at the Pore Scale with Evolving Fluid-Solid Interfaces: Review of Approaches and Benchmark Problem Set.*Comput. Geosci.*2020,*25*, 1285, DOI: 10.1007/s10596-019-09903-xThere is no corresponding record for this reference.**108**Tetteh, J. T.; Barati, R. Crude-Oil/Brine Interaction as a Recovery Mechanism for Low-Salinity Waterflooding of Carbonate Reservoirs.*SPE Reservoir Eval. Eng.*2019,*22*, 877, DOI: 10.2118/194006-pa108https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmsF2iug%253D%253D&md5=56495586590226a905ee18c1b35322a2Crude-oil/brine interaction as a recovery mechanism for low-salinity waterflooding of carbonate reservoirsTetteh, Joel T.; Barati, RezaSPE Reservoir Evaluation & Engineering (2019), 22 (3), 877-896CODEN: SREEFG; ISSN:1930-0212. (Society of Petroleum Engineers)Low-salinity waterflooding in limestone formations has been less explored and hence less understood in enhanced-oil-recovery (EOR) literature. The mechanisms leading to improved recovery have been mostly attributed to wettability alteration, with less attention given to fluid/fluid-interaction mechanisms. In this work, we present a thorough investigation of the formation of water-in-oil microdispersions generated when low-salinity brine encounters crude oil and the suppressed snap-off effect caused by the presence of sulfate content in seawater-equiv.-salinity brines as recovery mechanisms in limestone rocks. Improved recovery by seawater brine was attributed to the changes in dynamic IFT measurement experienced using seawater brine as the continuous phase, compared with the use of LSW and formation-water-salinity (FWS) brine. Furthermore, the use of seawater as a displacing fluid succeeds in improving recovery because of its high surface elasticity suppressing the snap-off effect in the pore throat. We also present an easy and reliable mixing procedure representative of porous media, which could be used for screening brine and crude-oil samples for field application. Fluid/fluid interaction as well as high surface elasticity should be investigated as the causes of wettability alteration and improved recovery experienced by the use of LSW and seawater-salinity (SWS) brines interacting with limestone formations, resp.**109**Tetteh, J. T.; Alimoradi, S.; Brady, P. V.; Barati, R. G. Electrokinetics at the Limestone Surface: Understanding the Role of Ions in Modified Salinity Waterflooding.*J. Mol. Liq.*2020,*297*, 111868, DOI: 10.1016/j.molliq.2019.111868109https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFyrt7bO&md5=e0217208becbeafe29710c5a738d0c05Tetteh, Joel T.; Alimoradi, Sirwan; Brady, Patrick V.; Barati Ghahfarokhi, RezaJournal of Molecular Liquids (2020), 297 (), 111868CODEN: JMLIDT; ISSN:0167-7322. (Elsevier B.V.)**110**Hiorth, A.; Cathles, L. M.; Madland, M. V. The Impact of Pore Water Chemistry on Carbonate Surface Charge and Oil Wettability.*Transp. Porous Media*2010,*85*, 1– 21, DOI: 10.1007/s11242-010-9543-6110https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXhtFOnsbvE&md5=c4b7be80ec793f655012c5ec6fb00b7cThe Impact of Pore Water Chemistry on Carbonate Surface Charge and Oil WettabilityHiorth, A.; Cathles, L. M.; Madland, M. V.Transport in Porous Media (2010), 85 (1), 1-21CODEN: TPMEEI; ISSN:0169-3913. (Springer)Water chem. has been shown exptl. to affect the stability of water films and the sorption of org. oil components on mineral surfaces. When oil is displaced by water, water chem. has been shown to impact oil recovery. At least two mechanisms could account for these effects, the water chem. could change the charge on the rock surface and affect the rock wettability, and/or changes in the water chem. could dissolve rock minerals and affect the rock wettability. The explanations need not be the same for oil displacement of water as for water imbibition and displacement of oil. This article investigates how water chem. affects surface charge and rock dissoln. in a pure calcium carbonate rock similar to the Stevns Klint chalk by constructing and applying a chem. model that couples bulk aq. and surface chem. and also addresses mineral pptn. and dissoln. We perform calcns. for seawater and formation water for temps. between 70 and 130°C. The model we construct accurately predicts the surface potential of calcite and the adsorption of sulfate ions from the pore water. The surface potential changes are not able to explain the obsd. changes in oil recovery caused by changes in pore water chem. or temp. On the other hand, chem. dissoln. of calcite has the exptl. obsd. chem. and temp. dependence and could account for the exptl. recovery systematics. Based on this preliminary anal., we conclude that although surface potential may explain some aspects of the existing spontaneous imbibitions data set, mineral dissoln. appears to be the controlling factor.**111**Brady, P. V.; Krumhansl, J. L.; Mariner, P. E. Surface Complexation Modeling for Improved Oil Recovery. In*SPE Improved Oil Recovery Symposium*; Society of Petroleum Engineers, 2012; pp 14– 18.There is no corresponding record for this reference.**112**Mahani, H.; Keya, A. L.; Berg, S.; Nasralla, R. Electrokinetics of Carbonate/Brine Interface in Low-Salinity Waterflooding: Effect of Brine Salinity, Composition, Rock Type, and PH on Zeta-Potential and a Surface-Complexation Model.*SPE J.*2017,*22*, 053– 068, DOI: 10.2118/181745-pa112https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXjtVGmsLo%253D&md5=881b93f38ddc3b0aa447446d7cf161b7Electrokinetics of carbonate/brine interface in low-salinity waterflooding: effect of brine salinity, composition, rock type, and pH on ξ-potential and a surface-complexation modelMahani, Hassan; Keya, Arsene Levy; Berg, Steffen; Nasralla, RamezSPE Journal (Society of Petroleum Engineers) (2017), 22 (1), 53-68CODEN: SPJRFW; ISSN:1930-0220. (Society of Petroleum Engineers)Lab. studies have shown that wettability of carbonate rock can be altered to a less-oil-wetting state by manipulation of brine compn. and redn. of salinity. Our recent study (Mahani et al. 2015b) suggests that surface-charge alteration is likely to be the driving mechanism of the low-salinity effect in carbonates. Various studies have already established the sensitivity of carbonate- surface charge to brine salinity, pH value, and potential-detg. ions in brines. However, in the majority of the studies, single-salt brines or model-carbonate rocks have been used and it is fairly unclear how natural rock reacts to reservoir-relevant brine as well as successive brine diln.; whether different types of carbonate-reservoir rocks exhibit different electrokinetic properties; and how the surface-charge behavior obtained at different brine salinities and pH values can be explained. This paper presents a comparative study aimed at gaining more insight into the electrokinetics of different types of carbonate rock. This is achieved by ξ-potential measurements on Iceland spar calcite and three reservoir-related rocks-Middle Eastern limestone, Stevns Klint chalk, and Silurian dolomite outcrop-over a wide range of salinity, brine compn., and pH values. With a view to arriving at a more-tractable approach, a surface-complexation model (SCM) implemented in PHREEQC software (Parkhurst and Appelo 2013) is developed to relate our understanding of the surface reactions to measured ξ-potentials. It was found that regardless of the rock type, the trends of ξ-potentials with salinity and pH are quite similar. For all cases, the surface charge was found to be pos. in high-salinity formation water (FW), which should favor oil-wetting. The ξ-potential successively decreased toward neg. values when the brine salinity was lowered to seawater (SW) level and dild. SW. At all salinities, the ξ-potential showed a strong dependence on pH, with pos. slope that remained so even with excessive diln. The sensitivity of the ξ-potential to pH change was often higher at lower salinities. The existing SCMs cannot predict the obsd. increase of ξ-potential with pH; therefore, a new model is proposed to capture this feature. According to modeling results, formation of surface species, particularly >CaSO4 and to a lower extent >CO4Ca and >CO4Mg, strongly influence the total surface charge. Increasing the pH turns the neg. charged moiety >CaSO4 into both neg. charged >CaCO3 and neutral>CaOH entities. (Note that throughout this paper, the symbol>indicates surface complexes.) This substitution reduces the neg. charge of the surface. The surface concn. of >CO3Ca and >COMg moieties changes little with change of pH. Nevertheless, besides similarities in ξ-potential trends, there exist notable differences in terms of magnitude and the isoelec. point (IEP), even between carbonates that are mainly composed of calcite. Among all the samples, chalk particles exhibited the most neg. surface charges, followed by limestone. In contrast to this, dolomite particles showed the most pos. ξ-potential, followed by calcite crystal. Overall, chalk particles exhibited the highest surface reactivity to pH and salinity change, whereas dolomite particles showed the lowest.**113**Tagavifar, M.; Jang, S. H.; Sharma, H.; Wang, D.; Chang, L. Y.; Mohanty, K.; Pope, G. A. Effect of PH on Adsorption of Anionic Surfactants on Limestone: Experimental Study and Surface Complexation Modeling.*Colloids Surf., A*2018,*538*, 549– 558, DOI: 10.1016/j.colsurfa.2017.11.050113https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhvVKlsr3M&md5=4156005d433d14918aee6762dd50a9dbEffect of pH on adsorption of anionic surfactants on limestone: Experimental study and surface complexation modelingTagavifar, M.; Jang, S. H.; Sharma, H.; Wang, D.; Chang, L. Y.; Mohanty, K.; Pope, G. A.Colloids and Surfaces, A: Physicochemical and Engineering Aspects (2018), 538 (), 549-558CODEN: CPEAEH; ISSN:0927-7757. (Elsevier B.V.)We investigate surfactant adsorption on a model carbonate rock over a wide range of pH and surfactant-to-solid ratios, by both an exptl. and a theor. approach, to obtain a quant. understanding of how mineral constituents affect the adsorption equil. and dynamics. To constrain and compare the relative adsorption affinity and the likely modes of attachment on mineral constituents as pH changes, we performed surface complexation calcns. using a two-surface multisite diffuse layer model. We propose the formation of two surface species on both the major (i.e., calcite) and trace (i.e., the oxide-like sites on the edges of clay platelets) minerals: a monodentate inner-sphere complex and a weak or hydrogen bonding complex. Our modeling results suggest that charge-regulated inner-sphere complexation is the dominant adsorption mechanism on the calcite and oxide-like sites at low pH values regardless of the surface loading. We found weak or hydrogen bond adsorption to be significant on the calcite surface, and this became the dominant adsorption mode at pH ∼10. While the adsorption on calcite increases with surface loading, adsorption on the oxide-like sites remains independent of surface loading. These results suggest that surfactant adsorption can be comparable on the abundant low-surface-area calcite and trace high-surface-area oxide-like sites.**114**Sanaei, A.; Tavassoli, S.; Sepehrnoori, K. Investigation of Modified Water Chemistry for Improved Oil Recovery: Application of DLVO Theory and Surface Complexation Model.*Colloids Surf., A*2019,*574*, 131– 145, DOI: 10.1016/j.colsurfa.2019.04.075114https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXosl2nsb4%253D&md5=afb5dfd43335963a549be9ee5fb918f9Sanaei, Alireza; Tavassoli, Shayan; Sepehrnoori, Kamy