Managing reservoir pressure is critical in large-scale geologic carbon sequestration (GCS), as pressure interference among multiple projects targeting the same geologic formation can significantly influence injection performance and long-term storage efficiency. This study evaluates how interference affects the ability of individual wells to access and utilize reservoir pore space. Using basin-scale dynamic reservoir simulations of the Williston Basin and Rate Transient Analysis (RTA) methods, we systematically assessed the role of well spacing, reservoir properties, and injection strategies on pressure evolution and storage outcomes. The results demonstrate that inter-well interference at distances of up to 40 km can reduce cumulative CO2 injection capacity by as much as 56% compared to isolated well scenarios. Decreasing well spacing from 40 km to 20 km exacerbates this effect, reducing the accessible pore volume by up to a 70%, a relationship captured by a second-order polynomial fit. Conversely, optimizing injection rates is a powerful mitigation strategy that can enhance pore volume accessibility by up to 170%, following a power-law relationship. Sensitivity analyses reveal that inherent reservoir properties (porosity and permeability) determine the magnitude of interference, whereas operational choices (well-spacing and injection strategy) govern the trend and temporal evolution of these effects. Furthermore, normalization across reservoir realizations showed consistent interference patterns for identical well configurations, reinforcing the generality and robustness of the findings. These results underscore the necessity of coordinated basin-scale planning among operators to minimize interference, consequently maximizing storage efficiency, and improving the economic viability of large-scale geologic carbon storage.

Dissolution-precipitation reactions play a critical role in subsurface processes such as carbon mineralization, yet the pore-scale mechanisms governing clogging and reactive surface utilization in diffusion-dominated fractures remain poorly constrained. Here, we extend the two-dimensional LBM3RT framework to a fully three-dimensional, multi-component advection-diffusion-reaction lattice Boltzmann model and apply it to a dual-fracture system comprising a main flow conduit and a dead-end branch. After verification against analytical diffusion-reaction solutions and previously published 2D crystal-growth morphologies, we simulate a simplified reaction network in which an in situ mineral dissolves and a secondary mineral precipitates on fracture walls in a diffusion-dominated dead-end channel, for example, the conversion of CaCO3 to BaCO3 in the presence of soluble barium. Systematic parameter studies show that a lower dissolution rate constant enhances utilization of reactive surfaces. A lower precipitation rate constant reduces clogging tendency. A smaller difference between their equilibrium constants reduces clogging tendency. Increasing the inlet concentration of the mineral-forming aqueous species deepens the penetration of the precipitation front while lowering the local clogging-index peak. Enlarging the dead-end aperture substantially boosts the total precipitation rate and the spatial extent of the reaction front. Finally, including an impermeable passivation layer arrests local dissolution-precipitation but promotes gradual migration of the reaction front toward the fracture tip, thereby improving long-term access to reactive mineral surfaces. Together, these results demonstrate that LBM3RT provides a mechanistic pore-scale tool for interpreting reactive transport with complex geochemistry and optimizing injected fluid composition, fracture geometry, and operating conditions in engineered carbon mineralization systems.

Correction for ‘Geologic hydrogen: a review of resource potential, subsurface dynamics, exploration, production, transportation, and research opportunities' by Shaowen Mao et al., Energy Environ. Sci. , 2025, 18 , 9991–10035, https://doi.org/10.1039/d5ee02910d.

Transitioning from passive exploration to active manufacturing: from mechanistic control to engineered iron-rich rock transformation into carbon-free geologic hydrogen.

Underground hydrogen storage (UHS) is a promising option to buffer variable renewable power and support the hydrogen economy. Yet this technology is early-stage, and key uncertainties remain about how coupled processes such as geochemical and geomechanical impacts affect long-term storage security. In this paper, we seek to integrate the results from a large, multi-scale research program to quantify core feasibility metrics and situate these studies within ongoing efforts. Through this work, we evaluate hydrogen recoverability during storage by assessing diffusive losses, losses to geochemical interactions and their subsequent impact on geomechanical properties, and losses to residual saturation during injection and withdrawal. Molecular and laboratory studies show that lithologic heterogeneity, pore geometry, and mineral surface chemistry govern hydrogen–rock interactions, controlling loss pathways. High-pressure coreflooding is used to estimate relative permeabilities and measure parameters needed for reservoir-scale sensitivity analyses. These core-scale experiments found early hydrogen breakthrough at low saturations driven by capillary and viscous fingering, which limits pore-space utilization at early times, while repeated injection and withdrawal cycles improve deliverability over time. Technoeconomic factors and purity requirements for end uses are also considered as part of our comprehensive feasibility analysis. Finally, we underscore the importance of geophysical monitoring and tailored injection strategies to maintain integrity and efficiency. Together, these results provide a quantitative foundation for safe, scalable deployment of UHS and highlight priorities for future integrated studies.

Underground hydrogen storage (UHS) is considered a promising technology for enhancing clean energy resilience. Deep saline aquifers, with their abundance and ample storage capacity, are favorable candidates for UHS. Although the technical feasibility of UHS in saline aquifers has been widely studied, the impact of aquifer salinity (ratio of dissolved salt mass to total brine mass) on storage performance remains poorly understood. To address this gap, we conduct high-fidelity reservoir simulations to evaluate the effects of salinity on three UHS performance metrics: maximum reservoir pressure buildup, liquid-gas ratio of produced fluids, and hydrogen withdrawal efficiency. Our results show that salinity strongly impacts UHS performance. Hydrogen injection desiccates the near-well region, causing salt precipitation that reduces porosity and permeability. In aquifers with salinity <= 10%, most precipitated halite is redissolvable, causing only minor permeability reductions. Within this range, increasing salinity decreases the produced liquid-gas ratio and improves hydrogen withdrawal efficiency without significantly increasing reservoir pressure buildup. By contrast, at salinities >= 15%, halite accumulates over successive storage cycles, clogging near-well zones, drastically reducing hydrogen withdrawal efficiency and elevating pressure buildup. These findings indicate that aquifer salinity is a critical factor for UHS performance and should be carefully considered in site selection and operational design.

Traditional physics-simulation based approaches for inverse modeling and forecasting in geologic CO2 sequestration (GCS) are very time consuming. For example, a single inverse modeling may take a few weeks for a largescale CO2 storage model without leveraging any high-performance computing. To speed up this process, we developed a novel approach that employs machine learning methods to integrate monitoring data into subsurface forecasts more rapidly than current physics-based inverse modeling workflows allow. These updated forecasts with the updated models from the inverse modeling process will be used to provide site operators with decision support by generating real-time performance metrics of CO2 storage (e.g., CO2 plume and pressure area of review). First, we developed a deep learning (DL) model to predict the pressure/saturation evolution in large-scale storage reservoirs. A feature coarsening technique was applied to extract the most representative information and perform the training and prediction at the coarse scale, and to further recover the resolution at the fine scale by 2D piecewise cubic interpolation. The accuracy of the feature coarsening-based DL model is validated with a reservoir model built upon a Clastic Shelf storage site. Thereafter, the feature coarsening-based DL model was utilized as forward model in the inverse modeling process where a classical data assimilation approach, ES-MDAGEO, was applied. The efficiency and effectiveness of the proposed DL-assisted workflow for large-scale inverse modeling and forecasting was demonstrated with the Clastic Shelf storage model.

Wettability alteration is considered one of the primary mechanisms for enhanced oil recovery. Within this class, low-salinity water (LSW) flooding is regarded as a promising method due to its advantages of low cost and environmental friendliness. However, its physicochemical effects at the oil-water-rock contact line, and their impact on contact line dynamics, remain poorly understood in carbonates. In this work, we propose a pore-scale wettability alteration model that exhibits physical scalability based on the Lattice Boltzmann Method, which couples multiphase flow, solute transport, physicochemical reactions and Cassie-Baxter effects. We have performed theoretical analysis to identify a key dimensionless parameter at the three-phase contact line. By examining spontaneous imbibition in a channel, we elucidated how adsorption kinetics at the contact line affect fluid dynamics. To assess the model's ability to interpret experimental phenomena, we compared the simulation results to the experimental results of oil droplet movement responding to LSW on carbonate substrates. We also simulated multiphase flow in a carbonate microfluidic chip which agrees well with the experiments, indicating that the contact-line adsorption rate is markedly reduced in the vicinity of corners. The calibrated model parameters are then used to evaluate the impact of LSW-driven spontaneous imbibition on oil recovery in a 3D pore-scale model of an Estaillades limestone sample. The study revealed that, oil aging caused by the interaction between formation brine and carbonate rock, along with a high oil or water phase viscosity, can significantly weaken the low-salinity effect on oil recovery. In addition, with a high initial formation water saturation, the substantially longer three-phase contact line length can markedly enhance the low-salinity effect on oil recovery. Overall, this study addresses the multiscale contact line phenomena and underscores the pivotal role of the three-phase contact line in controlling wettability alteration and associated oil recovery in carbonates.

Fluid transport through fractured geological formations is strongly influenced by the redistribution of solutes at fracture intersections. In this study, we perform detailed numerical simulations of flow and scalar transport within the intersection of two smooth, planar fractures. The analysis focuses on the mixing ratio, the proportion of solute flux exiting along the outlet branch aligned with the primary inlet flow direction, relative to the total solute flux at the outlets. We systematically investigate how the mixing ratio varies with four key parameters: Peclet number, Reynolds number, flow rate ratio between outlet branches, and fracture intersection angle. Results show that the mixing ratio decreases with increasing Peclet number and outlet flow rate ratio, consistent with reduced diffusive spreading and enhanced streamline routing. While low Reynolds numbers have minimal impact, inertial effects at higher Reynolds numbers significantly increase the mixing ratio. Additionally, acute and obtuse intersection angles alter flow partitioning and modify the solute distribution at the outlets. These findings provide a quantitative basis for incorporating physically realistic mixing behavior—intermediate between complete mixing and streamline-following assumptions—into network-scale transport models. The results have direct relevance to subsurface energy systems, including geothermal energy production, carbon sequestration, and contaminant remediation.

Fracturing is a fundamental physics phenomena with broad relevance across multiple domains, ranging from infrastructure integrity, aerospace durability, reservoir production, and seismic events. We present a diverse dataset of simulated fracture evolution and material failure generated from two numerical solvers: the phase-field method and the combined finite-discrete element method (FDEM). These solvers differ in formulation, physical fidelity, and computational efficiency. The dataset includes five materials: PBX, anisotropic shale, tungsten, aluminum, and steel. For each, phase-field simulations span 400,000 cases: 200,000 under uniaxial tension and 200,000 under biaxial tension. The computationally expensive FDEM simulations include 90,000 split evenly among PBX, shale, and tungsten under uniaxial loading. All simulations begin with randomized initial fracture patterns. Each entry includes temporal data capturing fracture propagation dynamics. This comprehensive dataset is designed to support the development of foundational or surrogate machine learning approaches for predicting material failure. While no such models are introduced here, the dataset lays a robust foundation for advancing future research and innovation in these areas.

As global energy systems undergo a transition to cleaner alternatives, geologic hydrogen storage has emerged as a promising solution for large-scale energy storage. A critical factor in determining the feasibility of this approach is the effectiveness of caprock formations, such as shale, in preventing hydrogen migration. This study investigates the diffusion behavior of hydrogen through shale to assess its suitability as a caprock for geologic hydrogen storage. Using a novel double-seal core holder design and a through-diffusion apparatus, hydrogen diffusion was measured through shale rock from the Eagle Ford and Wolfcamp Formations under dry conditions. These measurements were complemented by microstructural and mineralogical analyses using low-pressure nitrogen adsorption and X-ray diffraction. The effective diffusion coefficient of hydrogen in these shale caprocks ranged from 2.51 x 10-8 to 9.85 x 10-8 m2/s. Notably, we observed that the diffusion behavior was more related to the pore network structure and could not be attributed to differences in the total pore volume between shale types alone. To further understand the role of pore network complexity, a fractal pore model was developed to correlate tortuosity with the fractal dimension of the pore structure (a measure of pore network complexity). The proposed model closely matched tortuosity values obtained from diffusion experiments, outperforming existing theoretical tortuosity-porosity correlations. These findings provide key quantitative parameters needed to assess the feasibility of geologic hydrogen storage as well as insights that can be applied to hydrogen storage in a range of geologic formations.

The complex phase behaviors of oil-CO2 immiscible diffusion and oil swelling in shale nanoscale space under the influence of competitive adsorption caused by fluid-solid interaction force are still unclear, which is significantly important for shale oil recovery and carbon sequestration. In this paper, a multi-relaxation-time lattice Boltzmann method integrating the two-phase two-component three-distribution Shan-Chen flow model and mass transfer model is established to simulate the CO2 diffusion through immiscible phase interfaces, oil-dissolved CO2 competitive adsorption on the mineral surfaces, and oil swelling in nanoporous media. The proposed model is verified by the microfluidic experiment to successfully capture the diffusion and swelling. Then, the effects of equilibrium dissolution concentration and competitive adsorption on CO2 diffusion/dissolution and oil swelling are studied. The results show that as the equilibrium dissolution concentration increases, the dissolution rate of CO2 is accelerated, resulting in the increase of oil swelling volume and the dissolved CO2 adsorption concentration. The mass of CO2 diffusing into the oil phase increases with CO2 adsorption capacity, but the oil swelling volume decreases because of the increased CO2 adsorption on mineral surfaces.

Relative permeability is a crucial two‐phase property in porous media that can be significantly impacted by wettability conditions. While traditional research has predominantly examined homogeneous wettability, this work explores the less studied pore‐size dependent (PSD) wettability, featured by a pore‐size dependent wettability distribution. Leveraging high‐fidelity Lattice Boltzmann simulations on CT‐scanned porous samples, we demonstrate how PSD wettability would impact relative permeability at the pore scale. Our findings reveal that the deviation of relative permeability from the homogeneous wettabilitity induced by PSD wettability can be 5%–20%. The deviation of relative permeability curves increases as the spanning range of the contact angle increases. We also find that this impact is less pronounced as the capillary number increases. By adopting a pore‐size‐dependent contact angle relationship, our approach provides a more accurate and nuanced understanding of how PSD wettability would impact two‐phase flow. These findings discovered at the pore scale may also provide valuable insights on relative permeability at the core to reservoir scales.

Accurately predicting when and how materials fail is critical to designing safe, reliable structures, mechanical systems, and engineered components that operate under stress. Yet, fracture behavior remains difficult to model across the diversity of materials, geometries, and loading conditions in real-world applications. While machine learning (ML) methods show promise, most models are trained on narrow datasets, lack robustness, and struggle to generalize. Meanwhile, physics-based simulators offer high-fidelity predictions but are fragmented across specialized methods and require substantial high-performance computing resources to explore the input space. To address these limitations, we present a data-driven foundation model for fracture prediction, a transformer-based architecture that operates across simulators, a wide range of materials (including plastic-bonded explosives, steel, aluminum, shale, and tungsten), and diverse loading conditions. The model supports both structured and unstructured meshes, combining them with large language model embeddings of textual input decks specifying material properties, boundary conditions, and solver settings. This multimodal input design enables flexible adaptation across simulation scenarios without changes to the model architecture. The trained model can be fine-tuned with minimal data on diverse downstream tasks, including time-to-failure estimation, modeling fracture evolution, and adapting to combined finite-discrete element method simulations. It also generalizes to unseen materials such as titanium and concrete, requiring as few as a single sample, dramatically reducing data needs compared to standard ML. Our results show that fracture prediction can be unified under a single model architecture, offering a scalable, extensible alternative to simulator-specific workflows.

Carbon mineralization in mafic and ultramafic rocks presents an opportunity for permanent carbon storage in the Earth's subsurface. However, due to their lower permeability, pre-existing fracture networks are key for mineralization to occur. Therefore, to fully develop this technology, a mechanistic understanding of the mineralization behavior in fractures with the consideration of hydrodynamic components is required. We use high-pressure microfluidics to investigate key mechanisms influencing dissolution-precipitation in a fracture network. The experiments were conducted in micromodels made of natural rocks with a comb-shaped flow channel to mimic a fracture network. This enabled studying the effect of injection rate on coupled dissolution-precipitation in advection and diffusion-dominated flow paths. We used gypsum carbonation as an analog reaction to allow for realistic experimental time frames due to its rapid reaction kinetics. The experimental work is coupled with high-fidelity numerical simulations to enhance our understanding of the parameters affecting the mineralization reaction. Our results demonstrate the importance of flow rate on the rate and nature of the gypsum carbonation reaction revealing that higher flow rates enable deeper penetration of the mineral precipitation front into the dead-end channels. This is an important finding since for sustained mineralization in a fracture network, precipitation in dead-ends while still allowing for flowing fractures is critical. Detailed characterization of the precipitates showed that lower flow rates led to porous and loose precipitates in the form of aragonite while higher flow rates mimicked supersaturation behavior leading to the formation of calcite. The reactive transport simulations further demonstrated the significance of flow velocity in advection-dominated channels to influence the efficiency of carbon mineralization in diffusion-dominated channels, potentially clogging of dead-end channels. These findings highlight the need for coupling chemical, mechanical, and hydrodynamic processes to evaluate the nature and extent of carbon mineralization in fractured media critical for permanent storage in mafic and ultramafic formations. This research further highlights the need for more investigation in potential subsurface fracture generation techniques to aid carbon mineralization.

The surge in global average temperatures has necessitated a reduction in the released carbon dioxide (CO2) levels from anthropogenic sources. Besides curtailing these emissions, there is a demand to remove the CO2 already accumulated in the atmosphere. Combined experimental and modeling studies can significantly aid in developing durable and less energy-intensive direct air capture (DAC) systems. Here, we develop a physics-based multiscale framework, systematically integrating a pore-scale Lattice-Boltzmann Method (LBM) model with a microstructure-aware macroscale breakthrough model, simulating coupled mass transfer and adsorption kinetics for a carbon-based CO2 sorbent. A screening of sorbent morphological parameters such as porosity and ratio of micropores/mesopores pertaining to hierarchical porous carbon nanofibers (CNFs) was conducted to delineate their influence on the adsorption performance. It was observed that a balanced pore network comprising mesopores and micropores yielded high adsorption efficiency owing to conducive utilization of surface sites and lower mass transport losses. Mechanistic interrogation at the macroscale revealed an early onset of saturation and an enhancement in the capacity for sorbent microstructures with a hierarchical porous architecture. Insights derived from this work can help formulate guidelines to inform the experimental design of porous CNFs.

This paper provides a comprehensive review of geologic hydrogen, covering its resource potential, origins, migration and trapping mechanisms, exploration techniques, production strategies, and pipeline transportation.

Capacitive deionization (CDI) is a promising technology that has gained interest for the desalination of brackish water. Hierarchically porous carbons are commonly used as electrodes for CDI due to their high surface areas and controlled pore size distributions that maximize ion adsorption capacity and rate. Electrospinning is an effective way of generating carbon nanofibers with high inter-fiber macroporosity that can be further modified to improve surface area, total pore volume, and pore size distribution. This work describes the use of sacrificial mesopore formers in tandem with a micropore etching technique to induce hierarchical porosity in electrospun fibers. Mesopores are formed via the dissolution of silica nanoparticles that are introduced into the fibers during the electrospinning step. After mesopore formation, micropores are etched into the resulting surface through KOH impregnation and thermal activation. This sequential technique creates a hierarchical network of pores from the inherent macroporosity of the fiber network, to the mesopores, and finally micropores to simultaneously maximize surface area and accessibility. Micropore formation is optimized to maximize specific surface area while maintaining physical integrity of the fibers. The combination of mesopores and micropores enables fast ion adsorption rates and capacity. Carbon fiber electrodes fabricated in this method achieve specific surface areas exceeding 1400 m (2) g (-1) , with pore volumes exceeding 1.0 cc g (-1) . The pore size distributions are highly controlled, with 80 % of total pore volume coming from pores <20 nm in radius. In 500 ppm constant voltage CDI tests, these fiber electrodes obtain a salt adsorption capacity of over 14 mg g (-1) at a salt adsorption rate of similar to 4 mg g (-1) min (-1) , showcasing the high capacity matched with high rate of these easily fabricated, inexpensive materials.

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics - in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our machine learning model exhibits strong performance (R^2=0.9) when tested on extremely diverse real domains at multiple scales.