Pore-scale Model Research Articles

Using X-ray computed tomography and pore-scale numerical modeling to study the role of heterogeneous rock surface wettability on hydrogen-brine two-phase flow in underground hydrogen storage

Underground hydrogen storage (UHS) is receiving increasing attention to address the challenges in hydrogen storage. A crucial aspect of UHS is understanding the transport of hydrogen in subsurface porous media. In this work, hydrogen core flooding experiments were conducted in a sandstone sample and then the pore-scale distribution of hydrogen and brine was visualized using high-resolution X-ray micro-computed tomography (micro-CT). After CT image processing, we measured the surface contact angles (CAs) on rock surfaces and found that the measured CAs followed a log-normal distribution. To investigate the influence of rock surface wettability on the transport properties in the hydrogen-brine-sandstone system, the lattice Boltzmann (LB) method was used to simulate two-phase flows with different surface CA distributions; hydrogen-brine relative permeability and capillary pressure curves through the primary drainage, imbibition, and secondary drainage processes were obtained by LB simulations. X-ray CT scanning showed that hydrogen resided in both large pores and small pores and pore throats after the primary drainage process (i.e., hydrogen displacing brine), whereas after the imbibition process (i.e., brine displacing hydrogen) hydrogen stayed primarily in large pores where the capillary pressure barriers were low. The LB two-phase flow modeling illustrated that water’s relative permeability increased whereas hydrogen’s relative permeability decreased when the core flooding moved from primary drainage to imbibition; in contrast, when the core flooding moved from imbibition to secondary drainage, water’s relative permeability decreased whereas hydrogen’s relative permeability increased. Decreasing the surface CA increased the capillary pressure and reduced the hydrogen residual rate, which indicates high hydrogen retrievability and thus is favorable for UHS practice. The change in CA’s standard deviation did not cause noticeable changes in water’s relative permeability curves, whereas it resulted in noticeable changes in hydrogen’s relative permeability curves. This work is the first study that utilizes X-ray micro-CT scanning and pore-scale multiphase flow modeling to quantitatively and comprehensively investigate hydrogen-brine two-phase flow properties under different rock surface wettability distributions. The research findings from this study will advance the understanding of hydrogen transport in an underground storage system and provide essential data for large-scale field studies in UHS.

The Effect of Porous Media Grain Size on non-Darcy Flow Behavior using Pore Scale Simulation

The impact of grain size of the porous media of the 3D images of a beadpack on the permeability values and non-Darcy flow behaviour is analyzed using pore-scale flow modelling utilizing the finite volume method. In this study, the sample used was a 3D image from random tight packing of spheres (beadpack) with a size of 250×250×250 voxels and a porosity of 0.36. Variation in grain size is carried out by upscaling the 3D image sample, so that this treatment affects the specific surface area value but does not change the porosity and tortuosity of the sample. Several variations of grain diameter include 100 μm, 125 μm, 150 μm, 175 μm, and 200 μm. In this study, we adopted PIMPLE algorithm, which combines SIMPLE and PISO algorithms to accomplish the Navier-Stokes equations of fluid dynamics in such porous media. The simulation results show that the permeability value exhibits an inverse relationship with the specific surface area. This correlation is in accordance with the Kozeny-Carman equation. In addition, the initiation of non-Darcy flow occurs at the reference length-based Reynolds number of 2.06 and the permeability-based Reynolds number of 0.049.

Buoyancy-Driven Dissolution Instability in a Horizontal Hele-Shaw Cell.

The dissolution of minerals within rock fractures is fundamental to many geological processes. Previous research on fracture dissolution has highlighted the significant role of buoyancy-driven convection leading to dissolution instability. Yet, the pore-scale mechanisms underlying this instability are poorly understood primarily due to the challenges in experimentally determining flow velocity and concentration fields. Here, we integrate pore-scale simulations with theoretical analysis to delve into the dissolution instability prompted by buoyancy-driven convection in a radial horizontal geometry. Initially, we develop a pore-scale modeling approach incorporating gravitational effects, subsequently validating it through experiments. We then employ pore-scale numerical simulations to elucidate the 3D intricacies of flow-dissolution dynamics. Our findings reveal that a simple criterion can delineate the condition for the onset of buoyancy-driven dissolution instability. If the characteristic length falls below a critical threshold, dissolution remains stable. Conversely, exceeding this threshold leads to two distinct regimes: the unstable regime of the confined domain affected by the initial aperture and the unstable regime of the semi-infinite domain independent of the initial aperture where the instability is no longer influenced by the lower boundary. We demonstrate that the pore-scale mechanism for this instability is due to the concentration boundary layer attaining a gravitationally unstable critical thickness. Through theoretical analysis of this layer and the time scales of diffusion and advection, we establish a theoretical model to predict where the dissolution instability occurs. This model aligns closely with our numerical simulations and experimental data across diverse conditions. Our work improves the understanding of buoyancy-driven dissolution instability in radial horizontal geometry. It is also of practical significance in understanding cavity formation in karst hydrology and preventing leaks in geological CO2 storage.

Unraveling residual trapping for geologic hydrogen storage and production using pore-scale modeling

Residual trapping is an important process that affects the efficiency of cyclic storage and withdrawal and in-situ production of hydrogen in geological media. In this study, we have conducted pore-scale modeling to investigate the effects of pore geometry and injection rate on the occurrence and efficiency of residual trapping via dead-end bypassing. We begin our theoretical and numerical analyses using a single rectangular pore to understand the key controls in bypassing. We further investigated two factors affecting bypassing: (a) a continuous cycle of injection-extraction of H2, and (b) variable pore geometry. Based on our pore-scale simulations, we found that: (a) a higher pore height/width ratio (h/w) and a higher injection rate cause more residual trapping, which is unfavorable for withdrawal of H2; (b) the trapping percentage increases with the h/w first and then decreases after h/w reaches 0.5; (c) and a converging-shaped pore can result in less trapping volume. Based on a theoretical comparison of the residual trapping behavior of H2 and CO2, we discuss the mechanisms that are applicable to CO2 residual trapping and the possibility of developing engineering controls of H2 storage and production.

P3D-BRNS v1.0.0: a three-dimensional, multiphase, multicomponent, pore-scale reactive transport modelling package for simulating biogeochemical processes in subsurface environments

Abstract. The porous microenvironment of soil offers various environmental functions which are governed by physical and reactive processes. Understanding reactive transport processes in porous media is essential for many natural systems (soils, aquifers, aquatic sediments or subsurface reservoirs) or technological processes (water treatment or ceramic and fuel cell technologies). In particular, in the vadose zone of the terrestrial subsurface the spatially and temporally varying saturation of the aqueous and the gas phase leads to systems that involve complex flow and transport processes as well as reactive transformations of chemical compounds in the porous material. To describe these interacting processes and their dynamics at the pore scale requires a well-suited modelling framework accounting for the proper description of all relevant processes at a high spatial resolution. Here we present P3D-BRNS as a new open-source modelling toolbox harnessing the core libraries of OpenFOAM and coupled externally to the Biogeochemical Reaction Network Simulator (BRNS). The native OpenFOAM volume-of-fluid solver is extended to have an improved representation of the fluid–fluid interface. The solvers are further developed to couple the reaction module which can be tailored for a specific reactive transport simulation. P3D-RBNS is benchmarked against three different flow and reactive transport processes: (1) fluid–fluid configuration in a capillary corner, (2) mass transfer across the fluid–fluid interface and (3) microbial growth with a high degree of accuracy. Our model allows for simulation of the spatio-temporal distribution of all biochemical species in the porous structure (obtained from μ-CT images), for conditions that are commonly found in the laboratory and environmental systems. With our coupled computational model, we provide a reliable and efficient tool for simulating multiphase, reactive transport in porous media.

An improved micro–macro model of multicomponent reactive transport for multimineral systems

The multiscale coupled reactive transport model has attracted increasing attention in recent years because it can reveal the different characteristics of the spatial structure evolution of porous media. A micro–macro model of multiscale coupled reaction transport has been derived from detailed pore-scale models by using the upscaling technique based on homogenization method. Because of its clear upscaling theory, it has been widely used. However, the current micro–macro model still has obvious defects in dealing with the geochemical reaction process in multimineral systems. To solve this problem, we coupled the model with existing geochemical models that have been widely verified so that the model has the ability to deal with multimineral reactions under complex hydrochemical conditions. The newly developed coupling model and the current model are used to simulate the same problem, which proves the superiority of the new model for geochemical reaction prediction. We extend the new model to a two-dimensional scanning electron microscope image, where a challenging medium geometry setup is handled well during simulation. At the same time, the new model also accurately captures the abrupt changes in macroscopic effective parameters caused by the change in geometric topological structure. Finally, we use the principle of mass conservation to quantify numerical errors caused by geometric evolution, ensuring the accuracy and validity of simulation results, which verifies the numerical scheme of the model to a certain extent.

Study of the efficiency of machine learning algorithms based on data of various rocks

Background: Absolute permeability plays an important role in studying the fluids flow in porous media during the development of oil and gas reservoirs, the injection of CO2 into reservoirs for storage, the monitoring of pollutants migration in underground aquifers, and the modeling of catalytic systems. Therefore, an accurate and fast evaluation of its values is an important task.
 Aim: The purpose of this article is to study the applicability of machine learning methods for predicting the absolute permeability of carbonate samples, as well as ways to improve the prediction of permeability.
 Materials and methods: The input data is 408 small volumes extracted from four cylindrical carbonate samples composed almost entirely of calcite. Input data includes total and connected porosity, specific surface area, radii of all and only connected pores, coordination number, throat radius and length, tortuosity, and absolute permeability. Permeability prediction is carried out using regression machine learning methods such as random forest, extremely random trees and extended gradient boosting. Parameters (data) of small volumes were determined using pore-scale modeling of water flow in their pore space applying a specialized Avizo software.
 Results: Data of small volumes extracted from fractured and non-fractured samples were analyzed, and the results showed that there are good relationships between many parameters of small volumes. For example, the connected and total porosity have a second-order polynomial relationship with a high correlation coefficient. Using the above-mentioned regression machine learning methods, absolute permeability values were predicted when input data divided into training and testing data in a ratio of 80/20 and 70/30.
 Conclusion: Using the logarithm of permeability instead of permeability itself and considering fractured and non-fractured samples separately, can increase the accuracy of absolute permeability prediction using the above-mentioned machine learning methods up to 90%. The extremely random trees method is the most accurate among the three machine learning methods considered for our task.

Phase diagram and permeability evolution for dissolving vertical fractures in a gravity field

Rock fractures always provide the main flow pathways in geological systems. When exposed to reactive flow, mineral dissolution causes different dissolution regimes and significantly impacts permeability change. Previous studies have examined dissolution regimes and permeability evolution in horizontal geometry, but dissolution dynamics and permeability changes of vertical fractures in a gravity field remain unexplored. Here, we conduct flow-dissolution experiments combined with a pore-scale modeling approach to investigate regime transition and permeability evolution in vertical fractures. We validate the modeling approach using experimental results and illustrate the importance of gravitational effects in dissolving vertical fractures. Our 3D numerical simulations reveal the regime transition, represented by a critical Richardson number Ri, from the forced convection regime to the buoyancy-driven convection regime as the importance of gravity increases. In the buoyancy-driven regime, gravitational effects promote the development of localized dissolution/flow channels. However, gravitational effects become negligible in the forced convection regime where wormholes and uniform patterns can be separated by the critical Pe number. Using the critical Ri and Pe numbers, we establish a phase diagram of dissolution regimes for vertical fractures in a gravity field, exhibiting good agreement with experiments. By correlating this phase diagram with permeability-aperture curves, we directly estimate the power law exponent of permeability evolution for each dissolution regime. This approach extends the classic phase diagram of dissolution regimes to consider gravitational effects on vertical fractures and provides a guide to directly estimate the parameters for permeability evolution, which is essential in reactive-transport modeling at the field scale.

Micro computed tomography images of capillary actions in rough and irregular granular materials

The present work investigates the effect of both surface roughness and particle morphology on the retention behaviour of granular materials via X-ray micro-computed tomography (µCT) observations. X-ray µCT images were taken on two types of spherical glass beads (i.e. smooth and rough) and two different sands (i.e. natural and roughened). Each sample was subjected to drainage and soaking paths consisting in a multiphase ‘static’ flow of potassium iodine (KI) brine (wetting phase) and dry air (non-wetting phase). Tomograms were obtained at different saturation states ranging from fully brine saturated to air dry conditions with 6.2 μm voxel size resolution. The data acquisition and pre-processing are here described while all data, a total of 48 tomograms, are made publicly available. The combined dataset offers new opportunities to study the influence of surface roughness and particle morphology on capillary actions as well as supporting validation of pore-scale models of multiphase flow in granular materials.

Significance of the natural convection to the heat transfer of porous media: A pore-scale study

Heat conduction in porous media is generally accompanied by the natural convection of the fluid within the pores driven by the temperature gradient. However, the criterion to determine the critical pore size of porous media where the natural convection cannot be neglected remains elusive. In this study, a pore-scale model is established to investigate the contributions of natural convection on the effective thermal conductivity (ETC) of porous media, which takes the reconstructed microstructures as the geometry input. We thus examine the effects of the porosity, thermal conductivity of the fluid and solid phases, and heating direction on the critical pore size where the natural convection starts to work. The results show that the critical pore size decreases with increasing porosity and heating temperature, but increases with the rise in the thermal conductivity of solid phase. Moreover, the critical pore size is significantly lower for the lateral heating, compared with the bottom heating. Finally, we proposed a new formula as an indicator to determine the significance of natural convection to the heat transfer in porous media.

Pore-scale modelling of particle transport in a porous bed

A computational study was performed of the transport of both non-adhesive and adhesive particles in a porous bed with a body-centred cubic (BCC) structure. Pore-scale simulation of the flow within the porous bed was achieved through combining the immersed boundary method and the lattice Boltzmann method. Particle transport is computed using an adhesive discrete-element method based on a multi-time-scale soft-sphere model. The fluid flow results are validated by comparison with experimental data for dimensionless permeability of flow in a porous bed of spheres. For computations with non-adhesive particles, the particles are observed to drift to the centre of ‘channels’ in the BCC array, within which most of the fluid flow occurs. The mechanism of this inward drift was found to be related to the phenomenon of oscillatory clustering, which is an inertial drift mechanism observed for particles in a corrugated channel. A measure for particle drift into these channels was developed, and the time rate of change of this measure was found to compare closely with an approximate theoretical prediction based on oscillatory clustering theory. The drift measure was observed to be limited at long time by hold-up of outlier particles caught in long-duration collisions with the fixed bed particles in regions of low fluid velocity magnitude. Simulations with adhesive particles exhibited marked increase in collision duration, as well as inhibition of the tendency to drift toward the flow channels due to adhesive hold-up.

Pore-scale imbibition patterns in layered porous media with fractures

The presence of fractures increases the difficulty of flow mechanisms analysis, and it remains unclear how fractures affect multiphase flow displacement in the layered rock matrix. Herein, a pore-scale imbibition model considering the layered matrix-fracture system is established using the phase-field method, where oil is displaced by a range of fluids with various properties. Two typical flow modes are carefully analyzed, depending on the locations of the fracture and the interfaces between different layers of the matrix: fracture is parallel to the interface (mode I), and it penetrates through the interface (mode II), which are dominated by the co-current imbibition and countercurrent imbibition mechanisms, respectively. Interestingly, the surface tension is found to be negatively correlated with the ultimate oil recovery rate for mode I and plays an opposite effect on that of mode II. For flow mode I, the conditions of lower injection rate, higher viscosity ratio, higher grain diameter ratio, and injection of the invading fluid from the larger pore throat size (positive direction flow) can improve oil recovery. For flow mode II, the fracture bifurcation angle has little effect on the positive direction flow, while it can significantly regulate the phase distribution in the negative direction flow. Based on scaling analysis of relating pore-filling events to displacement modes and the equilibrium relationship between capillary and viscous forces, two theoretical models are derived to predict the imbibition patterns, and the variation of the flow regime under various parameters in the typical layered matrix-fracture models is systematically concluded.

Estimating the interfacial permeability for flow into a poroelastic medium.

Boundary conditions between a porous solid and a fluid has been a long-standing problem in modeling porous media. For deformable poroelastic materials such as hydrogels, the question is further complicated by the elastic stress from the solid network. Recently, an interfacial permeability condition has been developed from the principle of positive energy dissipation on the hydrogel-fluid interface. Although this boundary condition has been used in flow computations and yielded reasonable predictions, it contains an interfacial permeability η as a phenomenological parameter. In this work, we use pore-scale models of flow into a periodic array of solid cylinders or parallel holes to determine η as a function of the pore size and porosity. This provides a means to evaluate the interfacial permeability for a wide range of poroelastic materials, including hydrogels, foams and biological tissues, to enable realistic flow simulations.

Predictable Local Polarization inside Electrodes By Three-Dimensional Multi-Scale Model in Flow Batteries

Redox flow batteries (RFBs) are regarded as one of the most promising grid-scale energy storage technologies, which can improve the applicability of wind and solar energy by leveling their fluctuating power output. In RFBs, the gas side reactions including hydrogen evolution reaction and oxygen evolution reaction reduce the efficiency and capacity of the batteries and the gas produced may even cause safety problems. Even if the voltage is controlled in practical applications to restrict the gas side reactions, it is still inevitable due to flow dead zones or insufficient reactants locally inside the electrode. Gas side reactions are usually caused by too high local overpotential, i.e., local over polarization. Therefore, in this work, we propose a multi-scale model that allows the rapid prediction of local polarization to limit and manage the gas side reactions. The multi-scale model contains a deep neural network, a pore network model, and a three-dimensional continuum model with the advantages of both accuracy and extensibility. By learning from the sample data provided by the cell-scale model and the pore-scale model, the deep neural network can quickly predict the local polarization under different operating conditions and give the distribution map of the entire electrode. Compared with conventional models, multi-scale models can take into account the geometry of electrode fibers and are suitable for electrode design and modification. Due to the calculation superiority of the pore network model, the multi-scale model can realize three-dimensional local polarization prediction, which enables a more accurate analysis of the internal uniformity of the electrode. Through the developed model, we explore the effects of the interdigitated flow field, flow rate, initial concentration and voltage on local polarization. It is shown that the multi-scale model can accurately predict local polarization. However, the promotion of flow rate cannot completely eliminate the occurrence of local over polarization.

Experimental and Numerical Study of the Effect of Rock Dissolution on Absolute Permeability of Limestone Sub-Samples

Permeability is a key transport property of porous materials, and its accurate evaluation is relevant when studying applied tasks, such as CO2 injection into reservoirs and investigating groundwater quality. This study examines the dependence of permeability on total and connected porosity, hydraulic tortuosity, specific surface area, and mean pore radius based on the data of 408 cubic sub-volumes extracted from heterogeneous and naturally fractured cylindrical carbonate samples, before and after injection of HCl solutions. These parameters were computed using pore-scale modeling of fluid flow. Our results show that permeability correlates well with porosity and mean pore radius, with correlation coefficients of R2≈0.65−0.79 for heterogeneous samples. It was found that the presence of natural fractures significantly influenced the relationship between permeability and other parameters. The relationship between permeability k, tortuosity τ, and specific surface area S is described by the power laws k~τ−α and k~S−β, with coefficients α and β substantially exceeding those in the Kozeny–Carman equation. It was also found that there is a parabolic relationship between connected and total porosities, both before and after rock dissolution with R2≈0.96−0.99. This allowed for an estimation of percolation threshold porosity in accordance with the literature data.

Transport properties evaluation of pore-scale GDLs for PEMFC using orthogonal design method

Gas diffusion layer (GDL) is an essential component of proton exchange membrane fuel cells, serving the functions of gas-water transport, thermal-electrical conduction and mechanical support. The various microstructural characteristics of the GDL have coupled and complex impact on transport properties, which is not comprehensively considered in previous studies. This study combines stochastically reconstruction techniques, pore-scale modeling and orthogonal design method to evaluate the coupling effect of multiple microstructural characteristics on transport properties, and determine the impact degree of each microstructural characteristics, including fiber diameter, porosity, GDL thickness, fiber orientation coefficient, binder and PTFE content. Finally, new mathematical models are developed and validated to predict and optimize the anisotropic transport properties accurately and rapidly by considering the coupling effect of multiple microstructural characteristics. The results showed that porosity has higher impact degree on gas diffusion and heat conduction than other microstructural characteristics. The combinations of microstructural parameters are optimized to achieve higher performance, with thermal conductivity and gas diffusivity increased by ≥139% and 62%, respectively. The prediction mathematical models are validated with the error ranging from 1% to 8%, which can predict transport properties and optimize the GDL microstructure accurately and rapidly.

Pore-scale modeling and investigation on the effect of calendering on lithium-ion battery cathodes

The cathode of lithium-ion batteries (LIBs) is a porous electrode that has a crucial influence on cell performance and durability. In making the electrode, a metal foil is first coated with a cathode slurry and dried. The electrode then undergoes a hard-pressing by rollers, i.e., calendering process. In the present study, the effects of calendering on the cathode's effective transport properties and its impact on the battery's charging performance are investigated using a multiscale approach. Microscopic images of LiNi0.8Co0.1Mn0.1O2 samples are obtained using X-ray computed tomography and SEM. This material's three-dimensional microstructure is generated using a numerical reconstruction algorithm based on the distribution characteristics of active material, additives, and pores observed from the microscopic images. Pore-scale simulations are performed on models of various calendering ratios (CRs) to evaluate the pore distribution, effective lithium-ion diffusivity, and electrical conductivity of the cathode material at different CRs. The computed effective transport properties of the cathode are applied to a macroscopic model to study the charging performance of LIBs at various CRs. It is found that the battery performance increases with CR and reaches optimal electrochemical performance at CR = 20 %. When the cathode is calendered to CR = 35 %, the battery's charging performance deteriorates, and this effect is more pronounced at high charging rates. The present study demonstrates a multiscale approach combining experiment characterization and pore-scale simulation for investigating the effects of calendering on LIB cathode. This approach can be employed for the future optimization of LIB fabrication processes.

A pore-scale reactive transport modeling study for quorum sensing-driven biofilm dispersal in heterogeneous porous media

Microorganisms regulate the expression of energetically expensive phenotypes via a collective decision-making mechanism known as quorum sensing (QS). This study investigates the intricate dynamics of biofilm growth and QS-controlled biofilm dispersal in heterogeneous porous media, employing a pore-scale reactive transport modeling approach. Model simulations carried out under various fluid flow conditions and biofilm growth scenarios reveal that QS processes are influenced not only by the biomass density of biofilm colonies but also by a complex interplay between pore architecture, flow velocity, and the rates of biofilm growth and dispersal. This study demonstrates that pore architecture controls the initiation of QS processes and advection gives rise to oscillatory growth of biofilms. Such oscillation is suppressed if biofilm dynamics are in favor of sustaining a sufficiently high signal concentration, such as fast growth or slow dispersal rates. By establishing a mathematical framework, this study contributes to the fundamental understanding of QS-controlled biofilm dynamics in complex environments.

A pore-scale numerical framework for solute transport and dispersion in porous media

Pore-scale modeling plays a crucial role in understanding and upscaling solute transport behavior in porous media. Direct simulation offers the highest fidelity in resolving pore-scale fluid flow and mass transfer, nevertheless, with unacceptable computational costs for practical applications. Pore network models (PNM), on the other hand, provide an efficient alternative but with reduced accuracy in representing transport dynamics. In this study, we propose a new framework of pore network models that ensures both accuracy and efficiency, which reproduces the pore-scale shear dispersion effect by utilizing the pore-scale shear dispersion coefficient to calculate the diffusive mass exchange rate between network elements. The coefficient is determined based on the extension of Taylor-Aris theory that expands the classical Taylor-Aris theory to encompass the pre-asymptotic regime. Additionally, the framework adopts a physically representative pore network, where the conduit length and volume of network elements are determined based on local resistance equivalence. After verifying its accuracy and reliability, we conduct a series of numerical cases on tube networks, sphere packs, and sand packs. The breakthrough curves and concentration profiles obtained from our new model show good agreement with direct simulation results and experimental data, while the simulation outcomes of the models that rely on the molecular diffusion coefficient or Taylor dispersion coefficient for local mass exchange may exhibit significant errors. We finally demonstrate that the significance of pore-scale shear dispersion on solute transport weakens for tube networks with increasing degrees of geometrical disorder. The proposed model provides an accurate and efficient numerical framework for the study of solute transport and dispersion in porous media.

Pore-scale study of phase transitions and humins coking in cellulose hydrolyzed to levulinic acid

Hydrothermal depolymerization of cellulose has generated tremendous excitement as a promising pathway to sustainable chemistry. Among these biomass products, levulinic acid (LA), as one of the most promising platform chemicals with rich chemistry, can be used as various fuel additives or further refined into sustainable biofuel and high-value chemicals. However, hydrothermal conversion of cellulose into chemicals involves complex physicochemical processes, suffering currently from insufficient understanding of processes coupling mechanisms and carbon deposition caused by coking side reaction. Here, we developed a pore-scale multiphase reactive transport model based on the lattice Boltzmann method (LBM), coupling the multicomponent multiphase flow, heterogeneous reaction, cellulose dissolution, and humins coking, to systematically explore the conversion of cellulose to LA over acid catalyst. A relationship with a monotonic increase between the coking appearance time and H+ concentration has been found when the coking amount shows a linear decrease over acid concentration. Our results underscore the importance of in-depth elucidation of process coupling mechanisms for the establishment of efficient hydrothermal preparation strategies from cellulose to platform chemicals.