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 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.
Selected Porous Media Contributions from the 1st Iran InterPore Conference
Copyright (c) 2026 Hassan Mahani https://creativecommons.org/licenses/by-nc-nd/4.0
Pareto-based Multi-Objective Optimization of Underground Hydrogen Storage Operational Condition Utilizing Machine Learning-Based Surrogate Models
Underground hydrogen storage (UHS) in depleted hydrocarbon reservoirs, saline aquifers, and salt caverns has emerged as a promising solution for large-scale, long-term energy storage to support the energy transition. However, evaluating the feasibility of UHS requires addressing substantial uncertainties in geological, operational, and economic factors. Physics-based reservoir simulations, though accurate, are computationally expensive and impractical for exploring a wide range of operational scenarios and optimization problems. To overcome these challenges, this study employs an intelligent surrogate model constructed using data collected from Latin hypercube sampling and validated against detailed reservoir simulations to perform sensitivity analysis and formulate a multi-objective optimization framework. The primary objectives are to maximize hydrogen recovery factor and hydrogen purity in the produced gas, while also incorporating net present value (NPV) as an economic criterion. Multi-objective optimization is performed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which enables efficient exploration of the trade-offs between conflicting objectives and provides a Pareto front of non-dominated solutions. This Pareto-based decision-making framework offers valuable insights into selecting optimal storage strategies under uncertainty. The results highlight two key findings. First, pure nitrogen (N2) as a cushion gas yields higher hydrogen mole fraction in production stream compared to CO2 and CH4, because of favorable segregation and flow behavior in porous media. Second, the optimal cushion gas mixture was determined to be 45% N2, 40% CO2, and 15% CH4, balancing recovery and purity objectives. Nevertheless, from both economic and operational efficiency perspectives, a CO₂ fraction of 100% emerges as the optimal solution. Additionally, injecting from bottom perforations and producing from top perforations was identified as the most effective configuration due to the gravity override effect of hydrogen. Importantly, re-simulation of the Pareto-optimal solutions confirmed the robustness and reliability of the surrogate-based optimization approach. This study demonstrates the capability of coupling surrogate modeling with evolutionary multi-objective optimization to design cushion gas, optimize injection/withdrawal schemes, and maximize both technical and economic performance of large-scale underground hydrogen storage projects.
Copyright (c) 2026 Hossein Kheirollahi, Shahab Ayatollahi, Hassan Mahani https://creativecommons.org/licenses/by-nc-nd/4.0
Highlights from the new issue of InterPore Journal
As we open this new issue of the InterPore Journal, I am encouraged by how rapidly our field is developing and by the role the journal plays in this progress. Each contribution in this edition reflects not only technical advancement but also the expanding relevance of porous media research to a wide range of challenges. The work presented here demonstrates a shared commitment to curiosity, rigor, and collaboration, which are necessary to sustain our community and push the boundaries of what we can achieve together.
Copyright (c) 2025 Nima Shokri https://creativecommons.org/licenses/by-nc-nd/4.0
A Comparative SPH Analysis of Interaction Between Viscous Debris Flow and Two Types of Barriers with Basal Clearance
Debris flows are a type of geophysical flow and are known as one of the most destructive geohazards. To control and mitigate damage to residential areas and infrastructure downstream, rigid barriers are installed along the flow path to dissipate the momentum and energy of the flow. While basal clearance and curved barriers have shown potential for granular flows, their combined application to viscous debris flows has remained unstudied. Therefore, this study aims to incorporate the advantages of barriers with basal clearance and curved geometries for application to viscous debris flows. To this end, a series of seven numerical simulations were performed using the smoothed particle hydrodynamics (SPH) method in LS-DYNA[1] to compare the performance of vertical and curved barriers with a basal clearance in the second row. The results indicate that curved barriers enhance the interaction process through rebounding, reducing the maximum kinetic energy by 59% in the most effective scenario (CNB2) compared to free flow conditions. Consequently, a system featuring a curved barrier in the first row and a narrow basal clearance in the second row was found to be the most practical. Furthermore, a single curved barrier reduced momentum by 16% more than a single vertical wall, performing similarly to dual vertical systems. Finally, it is noted that where site constraints or costs preclude two-barrier systems, a well-designed single curved barrier offers a viable alternative. [1] https://lsdyna.ansys.com/
Copyright (c) 2026 Anahita Aghagoli, Hamed Sadeghi https://creativecommons.org/licenses/by-nc-nd/4.0
Design of a Laboratory Setup to Determine the Dispersion Parameter in a Single-Well Chemical Tracer Test
The Single Well Chemical Tracer Test (SWCTT) is a precise in-situ method for estimating residual oil saturation (Sor) based on the chromatographic delay between a partitioning tracer (ester) and its hydrolyzed product (alcohol). In this study, laboratory flooding experiments were designed and conducted in a glass-bead packed system at a constant temperature of 149°F to determine the dispersion and kinetic parameters of Ethyl Acetate (EtAc). The experiments were performed under two salinity conditions (5,000 ppm and 50,000 ppm) and simulated using a 1D model in the UTCHEM simulator with a 1 mm cell size. The results demonstrated that salinity significantly influences reaction kinetics and transport properties. Increasing salinity from 5,000 to 50,000 ppm resulted in an increase in the partition coefficient (KD) from 3 to 5.1 and the hydrolysis rate (KH) from 0.1 to 0.5/day. Furthermore, the hydrodynamic dispersion coefficient was determined through matching to be 0.003 ft2/day for the test conducted at a salinity of 5000 ppm and 0.01 ft2/day for the test conducted at a salinity of 50,000 ppm, resulting in accurate agreement between the simulated and experimental data. The Sor values of 0.2414 and 0.2303 obtained from the combined analysis of the model and experimental data showed excellent agreement with the reference value determined by material balance. This consistency serves as robust validation of the laboratory setup and confirms that the developed methodology reliably quantifies both Sor and hydrodynamic dispersion. Crucially, these findings establish a solid foundation for upscaling the methodology to pilot tests and reservoir-scale applications.
Single well chemical tracer test (SWCTT)
Copyright (c) 2026 Mostafa Ramezani, Rohaldin Miri https://creativecommons.org/licenses/by-nc-nd/4.0
Multiphase Image Segmentation of Naturally Fractured Media: Benchmarking Deep Learning and Conventional Approaches
Characterizing subsurface reservoirs, more specifically naturally fractured subsurface reservoir rocks, is essential for the study of subsurface reservoir properties. Image segmentation is an important aspect of digital rock physics (DRP) workflows. Traditional image segmentation techniques are less accurate than deep learning-based segmentation algorithms. In this paper, we investigate the segmentation accuracy of a convolutional neural network U-net and compare it with traditional methods of Watershed and multi-Otsu thresholding for multiphase segmentation of grayscale images from a naturally fractured coal sample. The segmentation target involved multiphase classification of the matrix, fully-filled fractures with minerals, and open fractures. The results reveal that U-net outperformed the others with an Intersection over Union metric of 94.9%, a Dice metric of 97%, and a Recall metric of 97.5%. The results support the importance of multiphase, deep learning-based segmentation techniques to support DRP studies of naturally fractured rocks.
Copyright (c) 2026 Behrad Tabrizipour, Saeid Sadeghnejad, Mastaneh Hajipour, Thorsten Schäfer https://creativecommons.org/licenses/by-nc-nd/4.0
Exploring the Inter-Relationship between Climatic Conditions and Heat-Water Transfer in Deformable Cracked Surface Strata
Climate-induced soil desiccation and cracking present escalating challenges to infrastructure stability, necessitating comprehensive investigations of soil-climate interactions. Hence, this study presents a thermo-hydro-mechanical (THM) modeling framework to investigate the response of desiccation-cracked soil to climatic variations. The model was constructed using field observations and statistical analysis and incorporated three years of meteorological data from a susceptible area in Iran. The analysis explored heat conduction, water advection, mechanical deformation, and crack evolution under prolonged environmental conditions. Results revealed that heat flux was highly dependent on temperature gradients and crack geometry. Heat migrates upward from the subsurface when air temperature is lower than soil temperature or during rainfall, conditions typically associated with wet or cold seasons, whereas during warmer periods heat is transferred into the soil. Water flux exhibited strong seasonal variability, with rainfall-driven infiltration through the surface and enhanced evaporation near cracks during dry periods. During rainfall, water flow velocity in soil pores was at least 50 times higher than during warm weather. Moreover, a direct relationship between volumetric strain, temperature, and relative humidity was identified. Crack deformation intensified, and long-term climatic exposure resulted in cumulative shrinkage, increasing on average from 12.5% in the first year to 17.5% in the third year.
Copyright (c) 2026 Milad Jabbarzadeh, Hamed Sadeghi https://creativecommons.org/licenses/by-nc-nd/4.0
MAGNET: Medial Axis Guided Network Extraction Tool
Pore network models are useful for studying transport in porous materials in a computationally efficient way. Extraction of networks from volumetric images has evolved over the years, starting with medial axis-based approaches to more recent watershed segmentation. This paper reconsiders the classic medial axis method, which offers several advantages such as speed and topological correctness, and develops a modernized, updated, and improved version. The new method is named Medial Axis Guided Network Extraction Tool (MAGNET). It works by analyzing the skeleton of a porous material to identify pore centers at junctions and endpoints. Additional pore bodies are found on long throats using two different approaches. This work includes an efficient tool for calculating the cross-sectional area of throats with irregular shape by using walkers with an infinite mean-free path to probe the geometry orthogonal to the medial axis at the point of the throat constriction. This extra step was critical for obtaining an equivalent diameter needed to calculate the permeability. Lastly, MAGNET was written with computational efficiency in mind. The skeletonization approach was itself 4.2X faster than the SNOW watershed segmentation for a 10003 image. Additionally, a parallelized skeletonization was applied by processing the image in blocks with sufficient overlap which resulted in a 5.5X speed-up compared to the serial approach. To validate the output, MAGNET was tested on a 4003 voxel image of a Berea sandstone, and the flow and capillary properties of the extracted network were compared to the results from SNOW and the lattice-Boltzmann method. Structural information such as pore and throat size distribution and mercury intrusion curves was compared, and noticeable similarity was achieved. Crucially, the permeability predicted by MAGNET was within 5% of the lattice-Boltzmann prediction on the same image.
Copyright (c) 2025 Michael McKague, Hamed Fathiannasab, Mohammad Amin Sadeghi, Jeff Gostick https://creativecommons.org/licenses/by-nc-nd/4.0
Heat-activated Epoxy Foam for Permeability Alteration in Fractured Geothermal Fields: Proof of Concept
Geothermal energy plays a growing role in the transition to renewable and carbon free energy sources. A challenge for many geothermal operations is how to enhance water-rock heat exchange either by creation of new fractures in a tight rock, or by blocking short-circuiting large conduits. Here we report a novel approach for blocking large aperture (cm scale) fractures using heat-activated epoxy resin foam. The foam is injected as discrete inert resin droplets that are transported to regions of the geothermal field and activated upon reaching sufficiently high temperatures, where they undergo foaming and curing, thereby locally reducing permeability. In contrast to alternative methods for reducing rock permeability such as silicate gels or heat responsive polymer microbeads that target fractures of small apertures (< 0.1 mm), the epoxy foam can reduce permeability of fractures with large apertures (about 10 mm) while attaining mechanical strength and thermal stability. Results from laboratory aluminum-glass fracture models provide insights by visualizing the transport phase of resin droplets and their subsequent temperature-induced foaming and curing transformations with associated flow pathway blocking. Modeling results for transport and foaming in a simple fracture considering rheological properties and foaming (volume expansion) behavior are compared with measurements of permeability changes. Challenges associated with upscaling to fracture networks and large transport distances of resin droplets are discussed.
Copyright (c) 2026 Dani Or, Rishi Parashar, Ying Yang, Manish Bishwokarma, Satish Karra, Yutong Cui https://creativecommons.org/licenses/by-nc-nd/4.0
Optimization of the Energy Extraction Process from Enhanced Geothermal Systems
Enhanced Geothermal Systems (EGS) have emerged as a viable solution for sustainable energy extraction from hot dry rock reservoirs. In this study, numerical simulations are employed to investigate the optimization of energy extraction from enhanced geothermal systems, focusing on the effects of fracture geometry, the number of fractures, and well spacing on overall system performance and profitability. By validating the model against previously established semi-analytical and analytical models, this research explores the importance of the mentioned parameters in determining cumulative energy production and economic outcomes across different project lifespans. The results demonstrate that increased well spacing enhances energy output by delaying thermal breakthrough and increasing the optimal injection rates. Although small fractures have limited thermal capacity, the primary influence of fracture geometry is to limit the applicable distance between the wells. Deeper wells require more pumping power, which reduces net energy gain and lowers the optimal injection rate. Multiple fractures along the well enhance energy production, particularly at higher injection rates and for smaller fractures. Economic analyses underscore the importance of operational design (including the well placement and injection rate) in maximizing profitability.
Copyright (c) 2026 Mohammad Norouzi Delaviz, Mozhdeh Sajjadi, Mohammad Emami Niri https://creativecommons.org/licenses/by-nc-nd/4.0
Petrophysical Characterization of Indiana Limestone Using Medical Dual-Energy Computed Tomography Technique: Insights into Porosity, Bulk Density, and Effective Atomic Number
Computed Tomography (CT) enables non-destructive 3D reconstruction of pore structures and rock properties mapping. Typically, such images are obtained from micro-CT (μm-scale) or synchrotron imaging (nm-scale). Despite their accuracy, these high-resolution imaging methods are expensive, time-consuming, and limited in sample size, affecting representative volume analysis. This work investigates an alternative approach using medical-CT (100 μm resolution) applying dual-energy CT technique (DECT) to characterize petrophysical properties (total porosity, bulk density, and effective atomic number) of six Indiana limestone samples (3.81 cm diameter, 4.88 cm length). Samples with porosities between 17.5% and 19.1% were scanned using paired high-energy (130 kV) and low-energy (80 kV) protocols, allowing DECT analysis to generate detailed 2D and 3D property maps. Additionally, the single-energy CT (SECT) technique, enhanced by subtracting images of the rock sample in saturated and dry conditions, improved the estimation of effective porosity. DECT and SECT results, processed with Python scripts and Avizo 3D, demonstrated average differences of 3.34% for bulk density, 5.30% for effective porosity, and 4.65% for effective atomic number compared to basic petrophysics measurements. Although artifacts from low-energy scans presented limitations, their impact can be reduced by optimizing acquisition parameters, improving the experimental setup, and applying reconstruction filtering techniques. Overall, this study highlights medical-CT as a fast, cost-effective method for analyzing larger samples, providing a practical alternative to traditional high-resolution imaging to estimate key petrophysical properties for the identification and visualization of heterogeneity on carbonates rocks.
Copyright (c) 2026 Walter Leonardo Flores Antelo, Janeth Alina Vidal Vargas, Rosangela Barros Zanoni Lopes Moreno 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
Static Elastic Properties of Rocks Obtained by X-ray Microtomography, Inverse Modeling and Surrogate Model
Digital rock analysis uses imaging techniques to obtain information concerning the internal structure of the rock. In the context of obtaining mechanical properties from digital images, much attention is given to determining the elastic parameters of rocks based on their mineralogical composition. While many applications for this type of simulation are available in the literature, the quantification of mineral property variations resulting from external influences has largely been overlooked. This work adopts an inverse modeling approach to estimate such elastic property variations in rocks. This methodology requires a predictive tool and an optimization algorithm to iteratively update the model’s parameters while reducing the discrepancies between modeled and measured data. In this study, a representative sandstone sample from the Botucatu Formation in Brazil was used to evaluate the methodology. Two case studies were considered: a synthetic example was designed for validation of the methodology while the second case applies real-world laboratory test data to estimate the properties that are most supported by the experimental evidence. A MATLAB code was built to integrate a finite element program and a genetic algorithm in a single framework. Additionally, an artificial neural network was used as a surrogate model to reduce the computational time of the numerical forward run. Overall, the results support the robustness of the approach and present a new alternative to obtain the mechanical properties of rock constituents at the pore scale.
Copyright (c) 2025 Ruan Gomes, Sergio Fontoura, Guilherme Righetto, Luiza Fernandes, Rafael Lopez, Rafaella Sampaio, Claudio Lima, Marcel Naumann, William Silva 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
Understanding Fracture Aperture and Permeability Evolution due to Carbonate Mineralization Utilizing 3D Printing
Caprock formations are a crucial part of subsurface-engineered systems. Composed largely of shale, caprocks act as natural barriers that prevent the upward migration of fluids, thereby ensuring the containment of stored substances in subsurface formations. Fractures in these formations are potential leakage pathways for stored fluids. Mineral precipitation reactions in these fractures, particularly calcite, can significantly restrict the fluid permeability, reducing leakage potential. However, predictive capabilities of mineral precipitation in fractures and associated permeability evolution are limited due to a lack of fundamental understanding of such reactions in natural samples, complicated by mineral heterogeneity and the complexity of the fracture structure. In this study, 3D-printed fracture samples are used to understand the impact of carbonate mineralization on fracture aperture and permeability evolution. Samples were printed using a digital light processing (DLP) 3D printer and commercial liquid resin. Calcite precipitation was first tested on printed 2D films before conducting plug flow column experiments aimed to understand fracture permeability changes due to mineral precipitation. Contact angle measurement and Fourier transform infrared (FTIR) spectroscopy on printed 2D films show evidence of a substantial amount of surface energy for calcite nucleation and precipitation. Surface topography analysis of printed fractured surfaces reveals comparable values, highlighting the high replicability of the printed samples. During the column experiments, the permeability reduces exponentially due to a decrease in fracture aperture. Reductions in fracture aperture estimated from effluent concentration and 3D X-ray computed tomography (CT) show comparable results. Moreover, 3D X-ray CT images suggest the impact of local flow velocities on precipitation. The insights gained from this research contribute to a deeper understanding of the permeability evolution due to carbonate mineralization in caprock formations.
Copyright (c) 2025 Md. Fahim Salek, Harrish Kumar Senthil Kumar, Abdullah Al Nahian, Samsul Mahmood, Bryan S. Beckingham, Kyle Schulze, Lauren E. Beckingham https://creativecommons.org/licenses/by-nc-nd/4.0
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
Modeling of Hydraulic Fracture Propagation in Real Heterogeneous Conglomerates from the Mahu Oilfield using the Global Cohesive Zone Method
Hydraulic fracturing enhances production in unconventional reservoirs but is challenged by reservoir heterogeneity, which complicates fracture propagation and reduces the efficiency of conglomerate reservoir development. The Mahu area in Xinjiang, a major petroleum reservoir in western China, is rich in oil and gas but features highly heterogeneous geological structures that complicate hydraulic fracturing and hydrocarbon extraction. In this study, we analyzed both homogeneous and heterogeneous core samples from the Mahu area. Real pore structures and gravel compositions were obtained using Computed Tomography (CT) and Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN). The Global Pore-Pressure Cohesive Zone (GPPCZ) model was applied to study the impact of heterogeneity on fracture propagation, considering differences in the mechanical properties of the matrix, gravel, and interfaces. Results showed that fractures tend to propagate in regions with lower critical fracture energy. In heterogeneous cores, fractures were more likely to deflect around gravel and bifurcate, forming complex networks. Peak propagation velocity occurred at interface contacts, but homogeneous cores exhibited faster propagation under the same stress conditions. This work provides theoretical support for optimizing hydraulic fracturing strategies in heterogeneous conglomerate reservoirs such as the complex Mahu area.
Copyright (c) 2025 Mingyang Zhang, Yanying Chen, Ming Yue, Ninghong Jia, Weifeng Lyu, Amer Alizadeh, Haoran Cheng, Chiyu Xie 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