Dynamics Of Porous Media Research Articles

Predicting interfacial tension in brine-hydrogen/cushion gas systems under subsurface conditions: Implications for hydrogen geo-storage

Underground hydrogen storage (UHS) critically relies on cushion gas to maintain pressure balance during injection and withdrawal cycles, prevent excessive water inflow, and expand storage capacity. Interfacial tension (IFT) between brine and hydrogen/cushion gas mixtures is a key factor affecting fluid dynamics in porous media. This study develops four machine learning models— Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP)—to predict IFT under geo-storage conditions. These models incorporate variables such as pressure, temperature, molality, overall gas density, and gas composition to evaluate the impact of different cushion gases. A group-based data splitting method enhances the realism of our tests by preventing information leakage between training and testing datasets. Shapley Additive Explanations (SHAP) reveal that while the MLP model prioritizes gas composition, the RF model focuses more on operational parameters like pressure and temperature, showing distinct predictive dynamics. The MLP model excels, achieving coefficients of determination (R2) of 0.96, root mean square error (RMSE) of 2.10 mN/m, and average absolute relative deviation (AARD) of 3.25%. This robustness positions the MLP model as a reliable tool for predicting IFT values between brine and hydrogen/cushion gas (es) mixtures beyond the confines of the studied dataset. The findings of this study present a promising approach to optimizing hydrogen geo-storage through accurate predictions of IFTs, offering significant implications for the advancement of energy storage technologies.

Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile

AbstractThe diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.

Analyzing Foam Injection Dynamics in Porous Media: A Combined Computational and Experimental Approach

The study of foam injection dynamics into porous media holds numerous applications, with its primary use being the optimization of the oil extraction process. Additionally, it finds utility in various other domains, including soil decontamination techniques. This study focuses on evaluating foam injection through low-cost experiments and computational models. The model integrates a classic two-phase immiscible fluid flow in a porous media framework, augmented by a mobility reduction factor to simulate foam’s impact on gas phase mobility. Simple experiments were conducted to assess water displacement using both gas and gas-foam injection methods. Results revealed that while gas injection tends to form preferential paths rapidly, leading to gas disruption, foam injection generates a more compact front, enhancing sweep efficiency by mitigating preferential path formation. The computational model successfully reproduced the experimental findings qualitatively. This research introduces a cost-effective experiment suitable for didactic purposes, illustrating complex concepts and underscoring the complementary nature of experimental and computational methodologies in advancing engineering techniques.

Quantifying the Effect of Pore‐Size Dependent Wettability on Relative Permeability Using Capillary Bundle Model

AbstractRelative permeability is a key parameter for characterizing the multiphase flow dynamics in porous media at macroscopic scale while it can be significantly impacted by wettability. Recently, it has been reported in microfluidic experiments that wettability is dependent on the pore size (Van Rooijen et al., 2022). To investigate the effect of pore‐size‐dependent wettability on relative permeability, we propose a theoretical framework informed by digital core samples to quantify the deviation of relative permeability curves due to wettability change. We find that the significance of impact is highly dependent on two factors: (i) the function between contact angle and pore size (ii) overall pore size distribution. Under linear function, this impact can be significant for tight porous media with a maximum deviation of 1,000%.

Analytical and Numerical Conceptions of Flame Propagation in Porous Media with a Generalization in Diffusion and Reaction Modeling

ABSTRACT In this paper, we expand on previous studies to further describe the flame propagation dynamics in porous media. We develop a model that incorporates a p-Laplacian operator to represent both fast and slow diffusion effects. Additionally, we include an advection term and a modified reaction term to address a broader range of chemical reactions related to flame spread. Based on analytical assessments, we first ascertain fundamental properties of the model’s solutions, such as regularity, boundedness, and uniqueness. We also examine stationary solutions, deriving their approximate analytical forms using regular perturbation methods and analyzing them within a Hamiltonian framework. Finally, we compare these analytical solutions with numerical ones to determine the spatial region where the approximate analytical solutions are valid within acceptable levels of error.

Transport and clogging of microplastic particles in porous media: Microscale experiments and statistical analysis

In recent years, the migration and distribution of microplastics (MPs) in the natural environment have garnered worldwide attention. However, little is known about the transport and intercept of MPs in infiltration systems. In infiltration systems, MPs could affect the flow through porous media, leading to complex flow and removal dynamics in various engineering applications. This will threaten the ecosystem and human health due to the characteristics of MPs. In this study, a two-dimensional porous media flow cell was developed to visualize the transport and intercept of microplastic particles in porous media. Statistical data on pore characteristics were gathered by tracking changes in pore clogging state under different particle concentrations and flow rates. It was found that the size ratio dt/dp was the critical factor influencing pore throat clogging probability. Pore throats were categorized into persistent-clogging, occasional-clogging, and non-clogging based on their clogging probability at different dt/dp. Additionally, the parameter dt/dpU that distinguished the occasional-clogging zone from the non-clogging zone decreased with increasing particle concentration. The clogging probability in the occasional-clogging zone was influenced by dt/dp, particle flux, and flow velocity in the pore throat. Furthermore, two distinct clogging mechanisms, independent and dependent clogging, were observed, determined by the states of neighboring pores. These findings have implications for assessing the interception performance of MPs in filtration systems. The results enhance our understanding of MPs transport and interception dynamics in porous media and contribute to the optimization of filtration system design and operation.

Analysis of thermal conductivity variation in magneto-hybrid nanofluids flow through porous medium with variable viscosity and slip boundary

Hybrid nanoparticles, which are nanoparticles composed of multiple materials or phases, find applications in various fields due to their unique properties resulting from the combination of different materials. In such fluids, viscosity changes significantly in response to alterations in temperature or pressure. For example, these fluids become less viscous as they are heated and more viscous as they are cooled. Similarly, in certain situations, the application of a temperature variation can cause a change in viscosity. This property is commonly observed in materials like polymers and some oils. The article aims to investigate the heat transfer and flow behavior of fluid from an elastic sheet of hybrid nanoparticles, considering the temperature-dependent variable viscosity and thermal conductivity. The obtained equations for the mathematical model are partial differential equations converted to ODEs by applying a suitable similarity transformation. The findings show that the magnetic field opposes fluid motion. Our main findings are that adding nanoparticles to the base fluid significantly increased its heat conductivity. This improvement has great potential for uses requiring effective heat transmission, especially in engineering systems where heat dissipation plays a crucial role. The study also reveals the complex relationships that influence thermal conductivity, including slip boundary effects, viscosity fluctuations, magnetohydrodynamic effects, and porous media dynamics. Understanding these interactions is critical for optimizing heat transport processes in porous media applications. Comprehending these interplays is essential for refining heat transport mechanisms in applications involving porous media. The graphical representations are used to explain the physical behavior of various model parameters. Previous outcomes are also contrasted with the current ones. The findings show that the magnetic field opposes fluid motion.We range the subsequent list of values for parameters in every graph unless indicated accordingly.ϵ=0.1,Pr=2,M=0.5,R=0.2,K=5,fw=0.1,s=0.1 ,λ=1,φ1=0.02,φ2=0.04.

Numerical Modeling of Fluid Flow Through Porous Media: A Modified Crank-Nicolson Approach to Burgers' Equation

This study presents a numerical modeling approach to investigate fluid flow through porous media, focusing on the application of the Modified Crank-Nicolson method to solve the Burgers' equation. The Burgers' equation, known for capturing non-linear features in fluid dynamics, serves as a pertinent model for porous media flow. The Modified Crank-Nicolson method, a variation of the traditional Crank-Nicolson technique, renowned for its stability and accuracy in solving parabolic partial differential equations, is employed to simulate the temporal evolution of fluid flow within the porous medium. Numerical experiments are conducted to explore the dynamic behavior of the system, considering various parameters and boundary conditions. The results showcase the efficacy of the Modified Crank-Nicolson approach in providing insights into the complex phenomena associated with fluid flow through porous media. This research contributes to the broader understanding of numerical methods in porous media dynamics and establishes a foundation for further investigations in related fields.

Linear enhancement of mechanical compliance by zeolite filling in a compact loudspeaker

A zeolite filling in the rear cavity of a compact loudspeaker enhances the sound pressure amplitude of the loudspeaker system with a frequency range up to ∼500 Hz, but its intrinsic mechanisms remain elusive. We theoretically consider the gas adsorption–desorption behavior on zeolite that can be dynamically perturbed by sound pressure from the diaphragm of the loudspeaker. By merging equivalent circuit theory with gas thermodynamic theory, a linear relationship between mechanical compliance and zeolite volume in the rear cavity is quantitatively modeled. Confirmed by electroacoustic impedance experiments, the linear relationship with a slope of 1.92 mF cm−3 reveals the intrinsic ability of porous material to enhance mechanical compliance by adsorption of gas onto the zeolite in the rear cavity. This study correlates the kinetic behavior of air molecules on zeolite and acoustic behavior; it offers a novel insight to improve the performance of a compact loudspeaker by simple filling with porous material and introduces a comprehensive method for evaluating gas adsorption–desorption dynamics in porous media.

Method SPH for numerical solution of filtering problems: including capillary forces in fluid flow simulation

This paper explores the theory of filtration within porous media, focusing on multicomponent mixtures’ dynamics and their numerical modeling. Key equations governing filtration, including Darcy’s law and mass conservation, are introduced. The concept of smoothed particle hydrodynamics (SPH) is presented as a powerful numerical technique for approximating these equations. SPH’s integral interpolation approach accommodates complex fluid-solid interactions and is applied to one-dimensional and two-dimensional test cases. Results demonstrate SPH’s effectiveness in capturing intricate dynamics and aligning with traditional methodologies. This study contributes to understanding fluid dynamics in porous media and underscores SPH’s potential for accurate simulations in diverse applications, from hydrocarbon reservoirs to environmental systems. The first section in your paper.

Analytical and Computational Approaches for Bi-Stable Reaction and p-Laplacian Diffusion Flame Dynamics in Porous Media

In this paper, we present a mathematical approach for studying the changes in pressure and temperature variables in flames. This conception extends beyond the traditional second-order Laplacian diffusion model by considering the p-Laplacian operator and a bi-stable reaction term, thereby providing a more generalized framework for flame diffusion analysis. Given the structure of our equations, we provide the boundedness and uniqueness of the solutions in a weak sense from both analytical and numerical approaches. We further reformulate the governing equations in the context of traveling wave solutions, applying singular geometric perturbation theory to derive the analytical expressions of these profiles. This theoretical development is complemented by numerical assessments, which not only validate our theoretical predictions, but also optimize the traveling wave speed to minimize the error between numerical and analytical solutions. Additionally, we explore self-similar structured solutions. The paper then concludes with a perspective on future research, with emphasis being placed on the need for experimental validation in laboratory settings. Such empirical studies could test the robustness of our model and allow for refinement based on actual measurements, thereby broadening the applicability and accuracy of our findings in practical scenarios.

CO2 soluble surfactants for carbon storage in carbonate saline aquifers with achievable injectivity: Implications from the continuous CO2 injection study

Continuous CO2 injection (CCI) into saline aquifers suffers from low sweep efficiency. While utilizing foam can effectively address this issue, a relatively potent foam design may undesirably reduce the injectivity of CO2. In this study, the potential of CO2-soluble surfactants assisted carbon storage in carbonate saline aquifers was evaluated by core flooding tests and numerical simulations. It is found that foam can be generated at the displacement front during continuous CO2 injection with 0.39 g/L surfactant (CCI + S, fg = 100 %). The CO2 saturation can reach 60 % after 1.0 pore volume of CCI + S, approximately 50 % higher than that of CCI. The maximum pressure gradient is around 1.5 psi/ft at an injection rate of 1.0 ft/d in 162 mD Indiana Limestone for CCI + S. The effects of surfactant concentration, foam quality, and rock permeability were also investigated by core flooding. Moreover, the influence of surfactant partitioning and adsorption on CO2 transport behavior was systematically evaluated by numerical simulation in synthetic and geological models, using modeling parameters that are realistic for field applications. The advantage of injecting surfactant with CO2 is more evident in heterogeneous saline aquifers. Such novel injection strategy provides a promising approach for carbon sequestration in saline aquifers by controlling the mobility of CO2 at the displacement front and simultaneously maintaining acceptable injectivity in the field. The CCI + S process may also impose less risk to the caprock. Expanding the potential CO2 storage sites to heterogeneous saline aquifers could be a game-changer. Our understanding of foam dynamics in porous media could also be advanced.

Modeling of permeability impairment dynamics in porous media: A machine learning approach

The prediction of clogging and permeability impairment dynamics in porous media is crucial for the optimization of various industrial and natural processes. This paper presents a novel machine learning-based approach for predicting the dynamics of throat clogging and permeability impairment due to fine migration within realistic porous media under varying hydro-physical conditions. A Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) numerical framework, employing a four-way coupling scheme, was used to generate the data for training and validation of the Machine Learning Model (MLM). One hundred and twenty distinct CFD-DEM simulations were performed to generate over 190,000 data points, at throat level, for the training of the MLM. Simulation cases encompassing ranges of porous media geometry, fine particle size, flow velocity, fine particle concentration, grains surface roughness, and fines and grains zeta potential. Geometries of porous media were extracted from high-resolution 3D images of natural sand obtained using micro-computed tomography imaging. The developed MLM predicts the temporal evolution of clogged throats and permeability impairment. The MLM was established by connecting three Machine Learning Sub-Models (MLSMs). The first is a throat-classification MLSM; which classifies the throats based on their location and size to identify clogged throats. Subsequently, a pore volume regression MLSM is implemented to identify the pore volume at which each clogged throat becomes clogged. Finally, the permeability impairment regression MLSM predicts the permeability reduction based on the clogged throat's information and pore volumes associated with clogging. The throats classification in the final MLM showed an accuracy of 95% in predicting clogged throats when compared to direct CFD-DEM simulations whereas the prediction of the permeability impairment had an R-squared value of 0.99. The MLM developed in this study stands as a robust framework for precisely quantifying key microscale parameters; where its predictions were used to quantify the significance of altering the hydro-physical parameters on the microscale parameters of the clogging dynamics. The proposed MLM provides an accurate and fast prediction of porous media clogging and permeability impairment dynamics, with potential applications in various industries, including oil and gas, environmental engineering, and material science.

High-Order Boussinesq Equations for Water Wave Propagation in Porous Media

To accurately capture wave dynamics in porous media, the higher-order Boussinesq-type equations for wave propagation in deep water are derived in this paper. Starting with the Laplace equations combined with the linear and nonlinear resistance force of the dynamic conditions on the free surface, the governing equations were formulated using various independent velocity variables, such as the depth-averaged velocity and the velocity at the still water level and at an arbitrary vertical position in the water column. The derived equations were then improved, and theoretical analyses were carried out to investigate the linear performances with respect to phase celerity and damping rate. It is shown that Boussinesq-type models with Padé [4, 4] dispersion can be applied in deep water. A numerical implementation for one-dimensional equations expressed with free surface elevation and depth-averaged velocity is presented. Solitary wave propagation in porous media was simulated, and the computed results were found to be generally in good agreement with the measurements.

Progress and Challenges of Integrated Machine Learning and Traditional Numerical Algorithms: Taking Reservoir Numerical Simulation as an Example

Machine learning techniques have garnered significant attention in various engineering disciplines due to their potential and benefits. Specifically, in reservoir numerical simulations, the core process revolves around solving the partial differential equations delineating oil, gas, and water flow dynamics in porous media. Discretizing these partial differential equations via numerical methods is one cornerstone of this simulation process. The synergy between traditional numerical methods and machine learning can enhance the precision of partial differential equation discretization. Moreover, machine learning algorithms can be employed to solve partial differential equations directly, yielding rapid convergence, heightened computational efficiency, and accuracies surpassing 95%. This manuscript offers an overview of the predominant numerical methods in reservoir simulations, focusing on integrating machine learning methodologies. The innovations in fusing deep learning techniques to solve reservoir partial differential equations are illuminated, coupled with a concise discussion of their inherent advantages and constraints. As machine learning continues to evolve, its conjunction with numerical methods is poised to be pivotal in addressing complex reservoir engineering challenges.

DarSIA: An Open-Source Python Toolbox for Two-Scale Image Processing of Dynamics in Porous Media

Understanding porous media flow is inherently a multi-scale challenge, where at the core lies the aggregation of pore-level processes to a continuum, or Darcy-scale, description. This challenge is directly mirrored in image processing, where pore-scale grains and interfaces may be clearly visible in the image, yet continuous Darcy-scale parameters may be what are desirable to quantify. Classical image processing is poorly adapted to this setting, as most techniques do not explicitly utilize the fact that the image contains explicit physical processes. Here, we extend classical image processing concepts to what we define as “physical images” of porous materials and processes within them. This is realized through the development of a new open-source image analysis toolbox specifically adapted to time-series of images of porous materials.

Effect of hydrodynamic dispersion on spherical reaction front dynamics in porous media

Effect of hydrodynamic dispersion on spherical reaction front dynamics in porous media

Using deep learning to accelerate magnetic resonance measurements of molecular exchange.

Real-time monitoring and quantitative measurement of molecular exchange between different microdomains are useful to characterize the local dynamics in porous media and biomedical applications of magnetic resonance. Diffusion exchange spectroscopy (DEXSY) is a noninvasive technique for such measurements. However, its application is largely limited by the involved long acquisition time and complex parameter estimation. In this study, we introduce a physics-guided deep neural network that accelerates DEXSY acquisition in a data-driven manner. The proposed method combines sampling pattern optimization and physical parameter estimation into a unified framework. Comprehensive simulations and experiments based on a two-site exchange system are conducted to demonstrate this new sampling optimization method in terms of accuracy, repeatability, and efficiency. This general framework can be adapted for other molecular exchange magnetic resonance measurements.

A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging.

Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.

Clogging and permeability reduction dynamics in porous media: A numerical simulation study

The dynamics of fine particle entrainment, transport, and deposition within pore systems, and in particular, the capacity for mobile fines to impair permeability within porous media is critical in many industrial applications. Considerable effort has been expended over the past several decades to identify and parametrize the governing factors that control permeability reduction, with studies employing a combination of physical experimentation and numerical simulation toward this aim. The objective of this work was to numerically investigate the impact of pore space geometry and fine sizes on the clogging dynamics of porous media. The clogging dynamics were characterized by the length of clogging zone (LCZ), the critical throat size of clogging (CTZC), the clogged fraction of throats (CFT), and the permeability reduction (PR). We implemented a computational fluid dynamics-discrete element method (CFD-DEM) numerical framework with a four-way coupling scheme to obtain insights into throat clogging and permeability reduction within heterogeneous porous media utilizing four different monodisperse suspensions. Geometries of porous media were extracted from 3D images of sand packs obtained using micro-computed tomography. The geometries had porosity values ranging from 0.35 to 0.57 and initial permeabilities between 1.35 and 26.32 μm2. CFD-DEM simulations were performed on each geometry four times, varying the injected fine particle size from 5 to 15 μm diameters. Findings indicate that pore systems with tortuosity <1.28 and angles of solid grains orientation larger than 46o tend to have shorter length clogging zones and greater permeability reduction. Furthermore, although systems with porosities higher than 0.48 tend to have a relatively high clogged fraction of throats, they undergo lower permeability reduction. It was also observed that within the studied pore systems, injection of fine particles larger than 7 μm into systems with aspect ratios higher than 2.5, results in a lower fraction of clogged throats. Additionally, for the studied pore networks,< 7 μm fine particles injected into porous media with a large pore body (> 66 μm) and throat (> 22 μm) radii tended to result in larger clogging zones and a greater critical throat size of clogging.