InterPore Journal is a new international, peer-reviewed, open-access journal, fully owned and published by The International Society for Porous Media (InterPore). Like the Society, our mission is to advance knowledge and research on porous media systems and their technical and industrial applications. We provide a trusted medium for sharing insights into the processes and phenomena involved, bringing together contributions from across disciplines to strengthen the global porous media community. The journal publishes original research articles, technical notes, review papers, open datasets and source codes, as well as short communications, commentaries, and opinion pieces. Topics span the full breadth of porous media science and technology, including imaging and characterization, physical and numerical modeling, data-driven methods, material design, and applications in geology, industry, environment, and biology. Our overarching goal is to serve as the focal point for the worldwide porous media community by publishing and freely sharing high-quality, multidisciplinary, and multiscale research. Articles are published under a Creative Commons license and appear four times per year with the online release of each issue, following final publication approval by the authors. Information on all published articles is shared through our bi-weekly e-newsletters (reaching 3000+ recipients) and across our social media channels. Stay Connected Keep up with the latest publications, news, and updates from InterPore Journal by subscribing to our newsletter and following us on social media. Join our vibrant online community and engage with fellow researchers worldwide: LinkedIn Facebook X YouTube Submit your manuscript today and join us in advancing the field of porous media science and technology. We look forward to receiving your contributions and continuing to support the growth of knowledge in 2025 and beyond.
InterPore: International Society for Porous Media
Unless otherwise stated above, this is an open access article published by InterPore under either the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0) (https://creativecommons.org/licenses/by-nc-nd/4.0/). Article metadata are available under the CCo license.
Highlights from the new issue of InterPore Journal
Copyright (c) 2025 Nima Shokri https://creativecommons.org/licenses/by-nc-nd/4.0
Suitability Mapping for Subsurface Floodwater Storage Schemes
The hydrological conditions a changing climate imposes require updated measures to address extreme water events sustainably. These must evolve from isolated solutions, such as water retention basins, to holistic management strategies that consider at least two situations simultaneously, such as floods and droughts. One strategy for sustainable groundwater management is managed aquifer recharge (MAR), which copes with decreasing groundwater levels by a targeted recharge of aquifers. Using high water as a recharge water source (Flood-MAR) can additionally cover the aspect of flood protection, resulting in a multi-beneficial solution for the region. This study evaluated the site suitability for subsurface floodwater storage schemes, which must focus more on assessing the existing aquifer characteristics and the specific flood dynamics in nearby rivers compared to known MAR schemes. Potential sites for underground flood storage are characterized by decreasing groundwater levels and, thus, frequent water scarcity, combined with eminent risks of flooding by a nearby river. Suitability, on the other hand, must take the aquifer, surface, and water source characteristics into account. In this study, we present a workflow for generating suitability maps for implementing subsurface floodwater storage systems with a geographic information system-based multi-criteria decision analysis (MCDA). The workflow was intentionally and exclusively based on publicly available data, was implemented in Python, and provided as open-source software. The resulting suitability maps spatially depict the feasibility of underground flood storage, and thus form the basis for the implementation planning of such projects. The approach was demonstrated for the administrative district of Swabia, Germany, where approximately 35% of the area was identified as suitable at varying levels. A sensitivity analysis of the assigned weights was applied to show the high robustness of the underlying data. The results highlighted the enormous potential of implementing such sustainable co-management schemes, which needs to be further concretized by on-site observations.
GIS-based multi-criteria decision analysis (GIS-MCDA)
Copyright (c) 2024 Lea Augustin, Thomas Baumann https://creativecommons.org/licenses/by-nc-nd/4.0
A Hydro-Geomechanical Porous-Media Model to Study Effects of Engineered Carbonate Precipitation in Faults
Hydro-geomechanical models are required to predict or understand the impact of subsurface engineering applications such as gas storage in geological formations. This study focuses on engineered carbonate precipitation through biomineralization in a fault zone of a cap-rock, aiming to reduce gas leakage from a reservoir. In addition to altering hydraulic properties such as porosity and permeability, precipitated carbonates also change the mechanical properties of the rock. We present a conceptual modeling approach implemented in the open-source simulator DuMux. After model verification, we applied the model to a CO2-storage scenario to investigate how biomineralization affects stress distribution within the rock, as well as how it may alter the risks of fault reactivation and induced seismic events. The generic study shows a tendency towards increased stiffness due to precipitated carbonate, which may cause shear failure events to occur earlier than in an untreated setup, while the magnitude of the seismicity is smaller.
Copyright (c) 2025 Yue Wang, Holger Class https://creativecommons.org/licenses/by-nc-nd/4.0
On the Interpretation of Unsteady State Experiments in Heterogeneous Rock by Stochastic Methods
Rock heterogeneity has a significant effect on immiscible displacement. This is especially true when the mobility ratio of the two fluids is unfavorable, favoring unstable displacement. However, this is not taken into account in the numerical analysis of classical core flooding experiments to quantify two-phase flow properties using Special Core Analysis (SCAL). Our approach combines the modern interpretation of SCAL data with experimental data measured on rock samples for which the homogeneity assumption - a prerequisite for SCAL experiments - can no longer apply due to their size and heterogeneity. In contrast to other studies that take heterogeneities into account, we focus on simple-to-perform unsteady-state experiments. We analyze these experiments by numerical interpretation using homogeneous and heterogeneous simulation domains and by introducing porosity-based heterogeneity and permeability as well as capillary scaling. In the current study, we first question the applicability of standard relative permeability measurements to heterogeneous rocks and fluids with an excessively high mobility ratio, such as for CO2-brine displacement in heterogeneous rocks. However, we show that they describe two-phase flow very well when porosity-based heterogeneity is taken into account, which is equivalent to downscaling. The study thus shows a way to fall back on established standard measurements if it should be possible to account for subgrid heterogeneities in SCAL workflows. To this end, we propose an approach based on steady-state experiments and appropriate sample selection.
Copyright (c) 2025 Omidreza Amrollahinasab, Boris Jammernegg, Siroos Azizmohammadi, Holger Ott https://creativecommons.org/licenses/by-nc-nd/4.0
Experimental Investigation of Solubility Trapping in 3D Printed Micromodels
Understanding interfacial mass transfer during dissolution of gas in a liquid is vital for optimizing large-scale carbon capture and storage operations. While the dissolution of CO2 bubbles in reservoir brine is a crucial mechanism towards safe CO2 storage, it is a process that occurs at the pore-scale and is not yet fully understood. Direct numerical simulation (DNS) models describing this type of dissolution exist and have been validated with semi-analytical models on simple cases like a rising bubble in a liquid column. However, DNS models have not been experimentally validated for more complicated scenarios such as dissolution of trapped CO2 bubbles in pore geometries where there are few experimental datasets. In this work, we present an experimental and numerical study of trapping and dissolution of CO2 bubbles in 3D printed micromodel geometries. We used 3D printing technology to generate three different geometries, a single cavity geometry, a triple cavity geometry, and a multiple channel geometry. To investigate the repeatability of the trapping and dissolution experimental results, each geometry was printed three times, and three identical experiments were performed for each geometry. The experiments were performed at a low capillary number (Ca = 3.33 x 10-6), representative of flow during CO2 storage applications. The DNS simulations were then performed and compared with the experimental results. Our results show experimental reproducibility and consistency in terms of CO2 trapping and the CO2 dissolution process. At such a low capillary number, our numerical simulator cannot model the process accurately due to parasitic currents and the strong time-step constraints associated with capillary waves. However, we show that, for the single and triple cavity geometry, the interfacial transfer and resulting bubble dissolution can be reproduced by a numerical strategy where the interfacial tension is divided by 100 to relax the capillary time-step constraints. The full experimental dataset is provided and can be used to benchmark and improve future numerical models.
Copyright (c) 2025 Alexandros Patsoukis Dimou, Mahdi Mansouri Boroujeni, Sophie Roman, Hannah Menke, Julien Maes https://creativecommons.org/licenses/by-nc-nd/4.0
3D Printing Reactive Porous Media: Calcite Precipitation Kinetics on Surface Functionalized Polymer Films and 3D-printed Cores
Mineral precipitation reactions in porous media can change the porosity and permeability of the rock formations. Predicting the rate of reaction and impacts on formation properties is challenging due to a lack of understanding of mineral precipitation reaction kinetics and mechanisms in porous media. This is furthermore challenging due to the highly heterogeneous nature of natural porous media. Here, we aim to develop a novel experimental platform leveraging 3D printing to facilitate replicable mineral precipitation experiments in controlled, heterogenous porous media systems. This requires fundamental understanding of the kinetics of mineral precipitation on the polymer materials used to fabricate the 3D printed porous media. In this work, we manipulate (via sulfonation) material surfaces (high impact polystyrene, HIPS) to promote calcite precipitation from supersaturated solutions to inform the design of synthetic subsurface systems. Calcite precipitation on HIPS films of varied surface sulfonation is confirmed using X-ray diffraction (XRD) analysis and weight-based precipitation experiments where increased precipitation with increased surface functionalization and solution saturation index are observed. This approach is then applied to 3D-printed porous media to enhance understanding of geochemical reactions, specifically calcite precipitation. Three dimensional images of Bentheimer Sandstone are used as the basis for 3D-printed porous media samples. Two 3D-printed samples were functionalized with acid to activate the surface and promote mineral precipitation. Functionalized and unfunctionalized samples underwent calcite precipitation core flooding experiments with oversaturated calcite solutions for 96 hours. Three dimensional X-ray micro-CT imaging revealed calcite growth in functionalized samples, with a calcite volume fraction of approximately 2.6% and a substantial reduction in porosity. Unfunctionalized samples exhibited diminished calcite precipitation and porosity changes. These findings demonstrate that reactive 3D-printed porous media can provide a versatile geochemical modeling and experimentation platform. Functionalizing 3D printed samples enhances reactivity, allowing investigations of mineral precipitation processes in complex porous media. This research highlights the potential for further exploration of 3D-printed media in various geochemical contexts.
Copyright (c) 2025 Harrish Kumar Senthil Kumar, Abdullah Al Nahian, Nailah Braziel, Zhoufan Shi, Madelyn Torrance, Vinita V. Shinde, Lauren E. Beckingham, Bryan Beckingham https://creativecommons.org/licenses/by-nc-nd/4.0
Experimental Study of Microbial Hydrogen Consumption Rates by Oleidesulfovibrio Alaskensis in Porous Media
The recovery efficiency of short- and long-term cyclic operations of porous media underground hydrogen storage (UHS) is a key parameter for successful implementation, but anaerobic microbes autochthonous in the storage formation can consume hydrogen and adversely influence hydrogen recoverability and storage efficiency. Here we have experimentally measured hydrogen consumption rates by a model sulphate-reducing bacterium (Oleidesulfovibrio alaskensis G20) in drainage-storage cycles that mimic porous media UHS. Laboratory tests were performed in cylindrical sand pack columns as storage site analogues (inner diameter: 14.7 mm, length: 51.4 mm) with an average porosity of 28% at conditions of 37°C and 1.15 bara. The storage capacity (initial hydrogen saturation in place) of each sand pack was also analyzed and compared against sterilized benchmarks. We observed an exponential decay in microbial hydrogen consumption between storage cycles: 28 ± 12% hydrogen was lost during the first cycle (with a peak average rate of 1.26 ± 0.12µmol/hr/cm3), compared with 10 ± 5% in the second cycle and 7 ± 3% in the third cycle. The cumulative loss across the three cycles amounted to 15 ± 6%, even though nutrient and carbon source concentrations were adequate for full hydrogen consumption in each cycle. The reduced microbial activity after the first storage cycle was explained by the observed increase in brine pH from an initial 7.5 to 8.4 ± 0.2 at the end of the last storage cycle. We observed improvement in the average hydrogen in place saturations after the first non-sterile storage cycles. Our experimental data enhances the understanding of microbial hydrogen loss during UHS and its impact on recovery and storage efficiency.
Copyright (c) 2025 Raymond Mushabe, Na Liu, Nicole Dopffel, Geir Ersland, Martin Fernø https://creativecommons.org/licenses/by-nc-nd/4.0
The subsurface is a critical foundation for society, providing essential resources such as water, minerals, and energy, while also offering storage capacity for resources and waste. Current subsurface use is expanding rapidly, leading to aggregated effects at regional scales ranging from tens to hundreds of kilometers. Despite their growing importance, current subsurface models remain limited to field-scale analysis and are insufficient for understanding regional scale impacts. This commentary advocates for a shift in subsurface modeling research, highlighting the importance of developing regional scale models that can assess the cumulative and interconnected effects of multiple subsurface operations. We outline a roadmap for developing the mathematical and numerical foundations necessary for regional scale subsurface analysis, highlighting the need for multiscale modeling, hybrid physics- and data-driven modeling approaches, and efficient computational tools.
Copyright (c) 2025 Inga Berre, Paola Antonietti, Rainer Helmig, Marc Hesse, Jan Martin Nordbotten https://creativecommons.org/licenses/by-nc-nd/4.0
Highlights From the New Issue of InterPore Journal
A novel robust optimization framework based on surrogate modeling for underground hydrogen storage in depleted natural gas reservoirs
Underground hydrogen storage (UHS) plays a vital role in global net-zero energy systems, enabling the storage of excess renewable energy for future use. However, physical reservoir model-based optimization for UHS system design and operation is computationally expensive due to complex geological properties and well-operational controls. This study developed a novel, efficient framework for UHS stochastic optimization to address this challenge, integrating advanced compositional reservoir simulation, accurate surrogate modeling, and stochastic optimization techniques. First, a base reservoir simulation model was developed to capture compositional fluid flow, hydrogen methanation reactions, gravity segregation, hysteresis, and capillary effects. To rapidly evaluate various well controls and reservoir configurations, convolutional neural network (CNN)-bi-directional long short-term memory (BiLSTM)-Attention models were trained as surrogate models using a comprehensive dataset generated from reservoir simulations. The CNN transforms three-dimensional (3D) geological fields into one-dimensional (1D) vectors, effectively capturing spatial features. The BiLSTM network learns the temporal evolution of the input features over time by processing them in both forward and backward directions. Subsequently, the attention mechanism enhances prediction accuracy by identifying and emphasizing the most significant features at critical time steps. The well-trained surrogate models were seamlessly integrated into the stochastic optimization framework based on the genetic algorithm, aiming to maximize the net present value (NPV) from UHS projects. The results demonstrate that the surrogate model exhibits satisfactory performance in the context of prediction accuracy, computational efficiency, and scalability. Notably, the newly developed framework based on surrogate models achieves an approximate 4878-fold speedup compared to an approach relying solely on reservoir simulation, while maintaining comparable accuracy. Overall, the proposed framework offers a promising solution for UHS optimization, providing valuable insights for the design and management of sustainable energy infrastructure.
Copyright (c) 2025 Zhilei Han, Zeeshan Tariq, Bicheng Yan https://creativecommons.org/licenses/by-nc-nd/4.0
Rubble Characteristics Associated with Room Collapse at the Waste Isolation Pilot Plant: Impact of Salt Clast Shapes and Size Distributions on the Depositional Pore System
The fluid transport properties of rubble associated with disposal room collapse within layered salt deposits at the Waste Isolation Pilot plant (WIPP) in southeastern New Mexico, USA are of concern when evaluating repository performance. Although properties of crushed salt have been studied extensively, salt rubble characteristics are less well known, while also being more difficult to characterize and model given the broader associated range in clast sizes. We describe a methodology for assessing rubble porosity and three-dimensional internal structure through deposition simulations that use high-fidelity renditions of clast shapes and that consider a broad range in clast sizes. We demonstrate the application of this approach using clast size and shape data that we collected on run of mine (ROM) material from WIPP, which represents the best available proxy for rubble material. Clast sizes from sieve analysis span ~1–100 mm with a mass weighted mean value of 13 mm. Simulated rubble deposits have interclast porosity values ranging from 34.1–38.6 vol% (n = 6). The largest clasts in these simulations tend to be underlain by the largest pores. Thus, although these clasts act as barriers for flow, they also are associated with highly permeable but localized flow paths. Scenarios involving alternative input configurations reveal that porosity values are highly sensitive to clast size variability (~10 vol% greater for nearly uniform compared to highly variable clast size distributions), clast shape (~10–13 vol% greater when using ROM shapes compared to spheres), and the extent of induced rearrangement (~3 vol% lower with limited rearrangement and ~10 vol% lower for extensive rearrangement). Porosity also is strongly affected by frictional coefficient values (~10 vol% lower for μ = 0.05 compared to μ = 1.0), but less so for values within the range of uncertainty for salt clasts (~2 vol% lower for μ = 0.62 compared to μ = 1.0).
Copyright (c) 2025 Robert Lander, Edward Matteo, Linda Bonnell, Thomas Dewers, Melisa Mills, James Guilkey, Chven Mitchell, John Stormont https://creativecommons.org/licenses/by-nc-nd/4.0
Anchored Physics-Informed Neural Network for Two-Phase Flow Simulation in Heterogeneous Porous Media
In this study, we propose a tensorization-anchoring strategy based on adaptive architectures to accelerate the computation of Enriched Physics-Informed Neural Networks (EPINN) for two-phase flow simulations. Specifically, we design an adaptive tensorization mechanism for the adjacency matrix embedding, the activation function, and the skip-gated connection in EPINN, which collectively expand the neural network's (NN) parameter space for learning more generalized patterns. Moreover, we developed an anchoring strategy by establishing Anchors-EPINN (An-EPINN). By detaching tensorization parameters from the computational graph and anchoring weighted nodes to fixed positions, the NN can benefit from tensorized fusion effects while reducing high-dimensional matrix calculations during forward and backward propagation, thereby enhancing simulation efficiency. This approach reduces execution time by 31.47% in homogeneous cases and 27.91% in heterogeneous cases, while maintaining higher computational accuracy.
Artificial intelligence for partial differential equations
Copyright (c) 2025 Jingqi Lin, Xia Yan, Kai Zhang, Zhao Zhang, Jun Yao https://creativecommons.org/licenses/by-nc-nd/4.0
Highlights From the Fourth issue of InterPore Journal
The Importance and Challenges Associated with Multi-scale Heterogeneity for Geological Storage
Understanding multi-scale heterogeneity in porous media has become increasingly critical as the world transitions from fossil fuel production to geological storage of CO2 and H2 for climate change mitigation. This commentary examines why small-scale heterogeneities have taken on a heightened importance in modeling subsurface fluid migration. We identify three key factors: increased public scrutiny and stricter permitting requirements for storage projects, different risk tolerances requiring long-term monitoring, and distinct flow physics compared to traditional oil and gas extraction. Drawing from current research, we demonstrate how current models consistently underestimate CO2 plume spread, likely due to inadequate representation of small-scale heterogeneities, which will also heavily impact H2 storage in porous rocks. We review the current state of research on incorporating small-scale heterogeneities into field scale models, discuss relevant spatial scales for both CO2 and H2 storage applications, and highlight promising directions for future research in this critical area.
Copyright (c) 2025 Catherine Spurin, Maja Rücker, Sally Benson, Catherine Callas, Nihal Darraj https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Effective Thermal Conductivity Predictions in Digital Porous Media Using Transfer Learning
Porous media beneath the Earth’s surface, including aquifers, oil and gas reservoirs, and geothermal systems, play a crucial role in various natural resource management and environmental engineering applications. The study of their physical properties, particularly thermo-physical properties like effective thermal conductivity (ETC), is essential for enhancing the efficiency of subsurface engineering technologies including nuclear waste disposal, geothermal energy utilization, and underground thermal energy storage. Traditionally, determining ETC has relied on either simplified empirical models, which often lack accuracy, or sophisticated laboratory experiments, which are time-consuming and resource intensive. The advent of three-dimensional (3D) imaging technologies has enabled digital characterization of subsurface media, but direct numerical simulations of ETC remain computationally prohibitive. In response to these challenges, we introduce a novel machine learning framework that leverages transfer learning to enhance the prediction of ETC in digital rock samples. Our approach utilizes state-of-the-art convolutional neural networks (CNNs), pre-trained on extensive datasets, and applies them to various porous media samples, including Berea sandstone, Bentheimer sandstone, and Ketton limestone. By employing transfer learning, we demonstrate that our models can achieve high prediction accuracy with significantly reduced training time, computational power, and data requirements. This study highlights the potential of transfer learning to advance the efficiency and accuracy of digital rock analysis, offering a promising tool for the rapid and reliable characterization of subsurface properties.
Copyright (c) 2025 Mohamed Elmorsy, Wael El-Dakhakhni, Benzhong Zhao https://creativecommons.org/licenses/by-nc-nd/4.0
A framework for simulating the partially miscible multi-component hydrocarbon fluids in porous media via the pseudo-potential lattice Boltzmann model
Retrograde condensation is a unique pressure-volume-temperature (PVT) behavior of partially multi-component hydrocarbon mixtures in porous media. However, some important physical properties, such as the component composition in each phase, the surface tension of the mixture, and the fluid wettability on specific rock surfaces at given temperatures, pressures, and molar compositions are difficult to evaluate dynamically in a laboratory. Previously, a multicomponent multiphase (MCMP) model was proposed to simulate the behavior of fluids composed of multiple components, such as gas condensate fluids or volatile oil fluids, where the components are partially miscible with each other. In this study, we extend the previously developed MCMP lattice Boltzmann (LB) model for partially miscible fluids by proposing a new framework to investigate the fluids’ phase behavior and flow dynamics under different phase conditions in porous media. The proposed framework integrates multiple lattice Boltzmann models to enable the convenient generation of desired wettability conditions on structural surfaces. A porous medium generated by the Voronoi tessellation (VT) was used in the case study to represent the pore-scale environment. The consistent scaling system in LB simulations was also briefly discussed. The proposed framework can enhance the understanding of the behavior of these fluids under varying conditions and can provide valuable insights into the qualitative evaluation of the pore-scale multiphase flow mechanism. Overall, this work contributes to the development of a computational framework for studying partially miscible hydrocarbon mixtures, which has important implications for the oil and gas industry.
Psuedo-potential lattice Boltzmann models
Copyright (c) 2024 Zhicheng W. Wang, Cheng Peng, Luis F. Ayala, Seyyed A. Hosseini https://creativecommons.org/licenses/by-nc-nd/4.0
Modeling Nanoparticle-Stabilized Foam Flow in Porous Media Accounting for Particle Retention and Permeability Reduction
This work presents a model for nanoparticle-stabilized foam flow in porous media, accounting for particle retention and the resulting permeability reduction. We present a semi-analytical solution under steady-state conditions, which allows for obtaining water saturation, foam apparent viscosity, and pressure drop profiles. We study different nanoparticle concentrations (in the presence and absence of salt) using retention parameters based on experimental data. When particle retention is neglected, the sweep efficiency of the porous medium improves compared to the case without nanoparticles, even at a low nanoparticle concentration (0.1 wt%). In contrast, when retention is accounted for, this enhancement is observed only at higher concentrations (0.5 wt% and 1.0 wt%). Neglecting nanoparticle retention generally underestimates pressure drop, especially in scenarios with significant retention. However, while retained nanoparticles increase pressure by reducing permeability, the loss of suspended nanoparticles decreases pressure by reducing the foam’s apparent viscosity. Consequently, when considering both nanoparticle loss and reduced permeability, the pressure drop is higher than in models that ignore retention. In contrast, omitting retention effects on permeability, the pressure drop is lower.
Copyright (c) 2025 Tatiana Danelon, Rouhi Farajzadeh, Pavel Bedrikovetsky, Grigori Chapiro https://creativecommons.org/licenses/by-nc-nd/4.0
The Influence of Density Driven Mixing Mechanisms on Ureolysis Induced Carbonate Precipitation
Engineered subsurface barriers with reduced porosity and permeability are critical for safe storage of CO2 and H2, for the prevention of pollutant transport, and for several other subsurface flow challenges. This study investigates enzyme-induced carbonate precipitation (EICP), a promising technique with the potential to achieve uniform precipitation in otherwise inaccessible regions, provided the mechanisms of pore-scale mixing are well understood. High-speed lab x-ray computed tomography and flow modelling were used to study the mechanisms of reagent mixing and precipitation. Our experiments show that initially, crystallization occurs homogeneously across grain surfaces, then localizes in­ pores with high enzyme concentrations. In these regions, we see crystal growth throughout the 65-minute experiment. Simulation of reagent injection produces a mixing front that matches the distribution of crystals seen in the experiments if we model mixing as a density driven flow. Overall, we see substantial reductions in simulated permeability (11-37%) depending on the efficiency of mixing. Our validated model allows us to predict and propose tailored injection strategies for optimizing mixing, bringing us closer to real-world deployment of EICP for subsurface barriers.
Microbially induced carbonate precipitation
Copyright (c) 2025 Philip J. Salter, James M. Minto, Jay Warnett, Katherine J. Dobson https://creativecommons.org/licenses/by-nc-nd/4.0
Integration of geochemistry into a geomechanical subsurface flow simulator
Accurately modeling geochemical reactions in subsurface flow is essential for understanding processes such as CO2 sequestration and contaminant transport. This paper presents a new numerical subsurface simulator (MF3D-GC) that combines flow, geomechanics, and geochemistry in an integrated and fully coupled manner. The simulator's capabilities were benchmarked by comparing it with other reactive-transport simulators. An adaptive tolerance method was implemented in the geochemistry module which reduced computing time while maintaining accuracy. User-defined kinetic models were used and coupled with changes in specific surface area, fluid saturation, temperature, and pH. The unique abilities of the model to couple geomechanics with geochemistry are highlighted. Our results show the importance of carefully selecting minerals and models to balance accuracy and computational efficiency. The model is used to simulate six different classes of geochemical flow problems which include flow, dissolution, precipitation, redox reactions, and diffusion with increasing levels of complexity. The potential applications of the model to CO2 sequestration, solution mining, geothermal energy production, and contaminant transport are briefly discussed.
Copyright (c) 2024 Miki Mura, Shuang Zheng, Mukul Sharma https://creativecommons.org/licenses/by-nc-nd/4.0
Inverse method to determine hydraulic conductivity from a velocity field using graph theory
A numerical inverse method called FlowPaths is presented to solve for the hydraulic conductivity field of an isotropic heterogeneous porous medium from a known specific discharge field (and constant-head boundary conditions). This method makes possible a new approach to reactive transport experiments, aimed at understanding the dynamic spatial and temporal evolution of hydraulic conductivity, which simultaneously record the evolving reaction and the evolving flow geometry. This inverse method assumes steady, two-dimensional flow through a square matrix of grid blocks. A graph-theoretical approach is used to find a set of flow paths through the porous medium using the known components of the specific discharge, where every vertex is traversed by at least one path from the upstream high-head boundary to the downstream low-head boundary. Darcy’s law is used to create an equation for the unknown head drop across each edge. Summation of these edge equations along each path through the network generates a set of linearly independent head-drop equations that is solved directly for the hydraulic conductivity field. FlowPaths is verified by generating 12,740 hydraulic conductivity fields of varying size and heterogeneity, calculating the corresponding specific discharge field for each, and then using that specific discharge field to estimate the underlying hydraulic conductivity field. When estimates from FlowPaths are compared to the simulated hydraulic conductivity fields, the inverse method is demonstrated to be accurate and numerically stable. Accordingly, within certain limitations, FlowPaths can be used in field or laboratory applications to find hydraulic conductivity from a known velocity field.
Copyright (c) 2024 Dr. Mont-Eton, Dr. Borgwardt, Dr. Mays https://creativecommons.org/licenses/by-nc-nd/4.0
High-fidelity experimental model verification for flow in fractured porous media
Mixed-dimensional mathematical models for flow in fractured media have been prevalent in the modeling community for almost two decades, utilizing the explicit representation of fractures by lower-dimensional manifolds embedded in the surrounding porous media. In this work, for the first time, direct qualitative and quantitative comparisons of mixed-dimensional models are drawn against laboratory experiments. Dedicated displacement experiments of steady-state laminar flow in fractured media are investigated using both high-resolution PET images as well as state-of-the-art numerical simulations.
Copyright (c) 2024 Jakub Wiktor Both, Bergit Brattekås, Eirik Keilegavlen, Martin Fernø, Jan Martin Nordbotten https://creativecommons.org/licenses/by-nc-nd/4.0
A line search algorithm for multiphysics problems with fracture deformation
Models for multiphysics problems often involve significant nonlinearities. When fracture contact mechanics are incorporated, discontinuous derivatives arise at the interfaces between open and closed fractures, or between sliding and sticking fractures. The resulting system of equations is highly challenging to solve. The naïve choice of Newton’s method frequently fails to converge, calling for more refined solution techniques such as line search methods. When dealing with strong nonlinearities and discontinuous derivatives, a global line search based on the magnitude of the residual of all equations is at best costly to evaluate and at worst fails to converge. We therefore suggest a cheap and reliable approach tailored to the discontinuities. Utilizing adaptive variable scaling, the algorithm uses a line search to identify the transition between contact states for each nonlinear iteration. Then, a solution update weight is chosen to ensure that fracture cells which change state do not move far beyond the transition point. We demonstrate the algorithm on a series of test cases for poromechanics and thermoporomechanics in fractured porous media. We consider both single- and multifracture cases, and study the importance of proper scaling of variables and equations.
Copyright (c) 2024 Ivar Stefansson https://creativecommons.org/licenses/by-nc-nd/4.0
Exploring chances and limitations of high resolution 3D-printing for guided water percolation in gas diffusion layers of polymer electrolyte fuel cells
In polymer electrolyte fuel cells (PEFC), the design of the porous materials is of significant interest due to their crucial role in determining the cell performance. Additive manufacturing (AM) has emerged as a feasible method for producing complex structures, offering precision and customization which cannot be achieved by conventional manufacturing techniques. This study explores the potential of high-resolution 3D-printing to produce gas diffusion layers (GDL) specifically tailored for polymer electrolyte fuel cells. We demonstrate the advantages of the method, such as its ability to create complex features in a 3-dimensional framework, as well as the challenges it faces, including the accuracy, reliability, and handling of the sample. The findings highlight both the promising capabilities and the current limitations of additive manufacturing of GDL, providing insights into future research directions and technological enhancements required to fully leverage AM to produce next generation porous materials.
Copyright (c) 2024 Tim Dörenkamp, Felix N. Büchi, Thomas J. Schmidt, Jens Eller https://creativecommons.org/licenses/by-nc/4.0/
The soil plastisphere: The nexus of microplastics, bacteria, and biofilms
Bacteria are one of the oldest life forms on Earth, dating back to more than 3.5 billion years ago. They control the global cycling of carbon, nitrogen, and oxygen. They provide plants, fungi and other organisms with the necessary nutrients and elements. They help us digest our food, protect us against pathogens, and even affect our behavior. Microplastics, however, have disrupted the bacterial ecosystems across the globe, from the soil to the oceans. Microplastics are tiny plastic particles formed as a result of the breakdown of the consumer products and plastic waste. Due to their stability and persistence, they can travel long distances in the soil and subsurface environments, ultimately making their way to the water resources, rivers, and oceans. In this journey, they interact with bacteria and other micro/macro-organisms, become ingested or colonized, and act as carriers for contaminants and pathogens. How and whether bacteria adapt to these new microplastic-rich ecosystems are open questions with far-reaching implications for the health of our planet and us. Therefore, there is an urgent need for improving our fundamental understanding of bacterial interactions with the microplastics in complex environments. In this commentary, we focus on the nexus of bacteria, biofilms, and microplastics, also known as the “plastisphere”, and discuss the challenges and opportunities.
Copyright (c) 2024 Amir Pahlavan https://creativecommons.org/licenses/by-nc-nd/4.0
Highlights from the third issue of InterPore Journal
Copyright (c) 2024 Nima Shokri https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning-Based Estimation of Oil Recovery Factor Using XGBoost: Insights from Classification and Data-Driven Analyses
In petroleum engineering, it is essential to determine the ultimate recovery factor (RF) particularly before exploitation and exploration. However, accurately estimating requires data that may not be necessarily available or measured at early stages of reservoir development. To rectify this, we applied machine learning (ML) to estimate oil RF from readily available features. To construct the ML models, we applied the XGBoost classification algorithm. Classification was chosen over regression because recovery factor is bounded from 0 to 1, much like probability. Three databases with various reservoir properties and recovery factors were used, leaving us with four different combinations to first train and test the ML models and then further evaluate them using an independent database including unseen data. Cross-validation with ten folds was applied on the training datasets to assess the effectiveness of the models. To evaluate the accuracy and reliability of the models, the accuracy, within-1 accuracy, precision, recall, macro-averaged f1 score and R2 were determined. Overall, results showed that the XGBoost classification algorithm could estimate the RF class with accuracies as high as 0.77 in the training datasets, 0.36 in the testing datasets and 0.24 in the independent databases used. We found that the reliability of the XGBoost classification model depended on the data in the training dataset, indicating that the ML models were database dependent. The feature importance analysis and the Shapley Additive exPlanations (SHAP) approach showed that the most important features were reserves, reservoir area and thickness.
Copyright (c) 2025 Alireza Roustazadeh, Frank Male, Behzad Ghanbarian, Mohammad B. Shadmand, Vahid Taslimitehrani, Larry W. Lake https://creativecommons.org/licenses/by-nc-nd/4.0
Inherent Errors in Current Core-Flooding Relative Permeability Data for Modelling Underground Hydrogen Storage
In the design and optimization of an underground hydrogen (H2) storage facility in an aquifer or other reservoir system, the H2/water relative permeabilities (RP) are the most critical two-phase data for input to numerical simulation. In this paper, we present a critical analysis of the published experimental H2/water RP functions in the literature. We present fine-grid simulations of H2 displacing water (denoted H2 --> water) using three of the most widely cited steady-state RP datasets (13, 20, 38) in a mildly heterogeneous permeability field, at a field length scale of ~100m. Since the viscosity ratio between water and H2 is (µw/µH2) ≈ 70, then at some length scale above a few meters, it is inevitable that the system must show immiscible viscous fingering of the H2 into the water phase. Indeed, the emergence of viscous fingering at some length scale is a “sense check” that the input data used in the simulations are correct, especially the H2/water relative permeability functions. In fact, none of the three published H2/water RP curves leads to viscous fingering. Instead, they all show stabilized flood fronts. The reasons for this are due to shortcomings of the (conventional) gas/liquid experimental methods used to obtain the RP functions. These methods yield RP functions at the wrong force balance between the capillary, gravity and viscous forces. For fingering to emerge, it is necessary to derive the viscous dominated RP functions. An alternative, more physically appropriate set of viscous dominated “fingering RP functions” is proposed and applied. When applied at the core scale, these new RP functions show fully dispersed flow, as they do when applied in a vertical (downwards) gravity stable displacement. However, the viscous fingering emerges naturally in horizontal flow as the length scale of the system increases and viscous forces become dominant.
Copyright (c) 2025 Gang Wang, Alan Beteta, Kenneth Sorbie, Eric Mackay https://creativecommons.org/licenses/by-nc-nd/4.0
Bio-nanocrystal Stabilized Biosurfactant Foams for Groundwater and Soil Remediation
Surfactants are crucial for reducing surface tension at liquid interfaces and have diverse applications in various fields. Solutions of surfactants can help remediate contaminated soil and groundwater. Foams, dispersions of gas bubbles within a liquid stabilized by surfactants, exhibit enhanced sweep properties that can improve the cleaning efficiency in groundwater remediation. However, surfactant-stabilized foams are thermodynamically unstable, and this poses challenges to their applications. Utilizing nanoparticles in conjunction with surfactants has shown promise in enhancing foam properties and contaminant recovery. Biosurfactants, which are surfactants naturally produced by microorganisms, offer a promising alternative to synthetic surfactants due to their biodegradability and low toxicity. In this paper, we investigated the use of biosurfactants, specifically rhamnolipids, in combination with bio-nanocrystals, namely cellulose nanocrystals (CNCs), to improve foaming properties and assess contamination recovery through foam flooding tests. The effect of pH and CNCs on the foaming properties of the rhamnolipid solution was also examined. Foam stability and foamability were evaluated using modified bulk foam tests, considering foam stability parameters and maximum foam volume. Constant shear rate and strain amplitude sweep tests were performed on different foams formulated at varying pH levels to assess viscosity and elasticity, and to distinguish the foam exhibiting superior properties. Furthermore, sand pack flooding experiments were conducted to assess the performance of rhamnolipid-stabilized foams in groundwater remediation. The results reveal pH-dependent variations in the foaming properties of the mixture. The findings suggest that an optimal, eco-friendly foam with maximum stability, foamability, and elasticity can be formulated by using 1000 mg/L rhamnolipid together with 1000 mg/L CNCs at a pH value of 10. Additionally, experiments demonstrate that foams with optimal properties can recover approximately 70% of contaminants (n-decane), representing more than three times the recovery achievable through the same amount of water injection.
Copyright (c) 2025 Alirza Orujov, Behbood Abedi, Karen Wawrousek, Saman Aryana https://creativecommons.org/licenses/by-nc-nd/4.0
Using Graph Neural Networks to Predict the Permeability of Porous Media
The permeability of porous media is often calculated using correlations or computationally expensive simulations. Several methods have been developed which use neural networks to predict porous media properties, but little work has been done on the development of a model that can handle porous media at the representative elementary volume (REV) scale. This work describes the framework for developing a graph neural network (GNN) to predict the permeability of porous media based on representative pore networks extracted from the structures, rather than representative structure volumes. This allows for consistent input sizes for the neural network, irrespective of the average pore size, which is more difficult when the entire voxelized structure is the model input. A GNN was trained to predict the permeability of porous media based on lattice Boltzmann method (LBM) simulations of the flow through the structures. The GNN showed a good agreement with the LBM simulations over samples with permeabilities spanning several orders of magnitude. The GNN was able to outperform the Carman-Kozeny equation with a mean squared error (MSE) for the unseen testing dataset of 0.00190 and a mean absolute error (MAE) of 0.0302 compared to an MSE of 1.125 and an MAE of 0.783 for the Carman-Kozeny equation, when comparing against the LBM ground truth. The inference time of the GNN alone was several orders of magnitude faster than the LBM simulations, and nearly 10 times faster when including the pore network extraction time needed for the GNN. This work demonstrates the potential of using GNNs to predict the permeability of representative porous media, and the benefits of using model architectures that take pore networks as the inputs.
Copyright (c) 2025 Jack M. I'Anson, Mark J. H. Simmons, E. Hugh Stitt, Robert W. Gallen https://creativecommons.org/licenses/by-nc-nd/4.0
POREMAPS: A Finite Difference Based Porous Media Anisotropic Permeability Solver for Stokes Flow
Porous materials are ubiquitous in various engineering and geological applications, where their permeability plays a critical role in viscous fluid flow and transport phenomena. Understanding and characterizing the microscale properties, the effective hydraulic parameters, and the anisotropy of porous materials are essential for the accurate modeling and predicting of fluid flow behavior. This study pursues the Digital Rock Physics approach to retrieve intrinsic permeability and its evolution in anisotropic configurations of porous media, which are subjected to pore space alterations. Therefore, we discuss the development and implementation of a computational framework based on the finite difference method to solve the pseudo-unsteady Stokes equations for fluid flow on the pore scale. We present an efficient and highly parallelized implementation of this numerical method for large voxel-based data sets originating from different image-based experimental setups. A comprehensive variety of benchmarks has been conducted to assess and evaluate the performance of the proposed solver. The solver’s compatibility with huge domain sizes generated by state-of-the-art imaging techniques is demonstrated. We investigate an open-cell foam undergoing deformation, observing that contrary to initial expectations, no anisotropy emerges. Further, we examine a microfluidic cell experiencing precipitation within its pore space, resulting in clear anisotropic development during the clogging process.
Copyright (c) 2025 David Krach, Matthias Ruf, Holger Steeb https://creativecommons.org/licenses/by-nc-nd/4.0