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Subsurface porous rocks have the potential to store large volumes of hydrogen (H$_2$) required for transitioning towards a H$_2$-based energy future. Understanding the flow and trapping behavior of H$_2$ in subsurface storage systems, which is influenced by pore-scale heterogeneities inherent to subsurface rocks, is crucial to reliably evaluate the storage efficiency of a geological formation. In this work, we performed 3D X-ray imaging and flow experiments to investigate the impact of pore-scale heterogeneity on H$_2$ distribution after its cyclic injection (drainage) and withdrawal (imbibition) from a layered rock sample, characterized by varying pore and throat sizes. Our findings reveal that even subtle variations in rock structure and properties significantly influence H$_2$ displacement and storage efficiency. During drainage, H$_2$ follows a path consisting of large pores and throats, bypassing the majority of the low permeability rock layer consisting of smaller pores and throats. This bypassing substantially reduces the H$_2$ storage capacity. Moreover, due to the varying pore and throat sizes in the layered sample, depending on the experimental flow strategy, we observe a higher H$_2$ saturation after imbibition compared to drainage, which is counterintuitive and opposite to that observed in homogeneous rocks. These findings emphasize that small-scale rock heterogeneity, which is often unaccounted for in reservoir-scale models, can play a vital role in the displacement and trapping of H$_2$ in subsurface porous media.

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the "FluidFlower"), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations.

The current conceptual model of mineral dissolution in porous media is comprised of three dissolution patterns (wormhole, compact, and uniform) - or regimes - that develop depending on the relative dominance of flow, diffusion, and reaction rate. Here, we examine the evolution of pore structure during acid injection using numerical simulations on two porous media structures of increasing complexity. We examine the boundaries between regimes and characterise the existence of a fourth regime called channeling, where already existing fast flow pathways are preferentially widened by dissolution. Channeling occurs in cases where the distribution in pore throat size results in orders of magnitude differences in flow rate for different flow pathways. This focusing of dissolution along only dominant flow paths induces an immediate, large change in permeability with a comparatively small change in porosity, resulting in a porosity-permeability relationship unlike any that has been previously seen. This work demonstrates that our current conceptual model of dissolution regimes must be modified to include channeling for accurate predictions of dissolution in applications such as geologic carbon storage and geothermal energy production.

The global commitment to achieve net-zero has led to increasing investment towards the production and usage of green hydrogen (H2).However, the massive quantity needed to match future demand will require new storage facilities. Underground storage of H2 is a potentially viable solution, but poses unique challenges due to the distinctive physical and chemical properties of H2, that have yet to be studied quantitatively in the subsurface environment. We have performed in situ X-ray flow experiments to investigate the fundamentals of pore-scale fluid displacement processes during H2 injection into an initially brine saturated Bentheimer sandstone sample. Two different injection schemes were followed, the displacement of H2 with H2-equilibrated brine and non-H2-equilibrated brine both at temperature and pressure conditions representative of deep underground reservoirs. H2 was found to be non-wetting to brine after both displacement cycles, with average contact angles between 53.72 and 52.72, respectively. We also found a higher recovery of H2 (43.1%) for non-H2-equilibrated brine compared to that of H2-equilibrated brine (31.6%), indicating potential dissolution of H2 in unequilibrated brine at reservoir conditions. Our results suggest that H2 storage may indeed be a suitable strategy for energy storage, but considerable further research is needed to fully comprehend the pore-scale interactions at reservoir conditions.

The permeability of a pore structure is typically described by stochastic representations of its geometrical attributes. Database-driven numerical solvers for large model domains can only accurately predict large-scale flow behaviour when they incorporate upscaled descriptions of that structure. The upscaling is particularly challenging for rocks with multimodal porosity structures such as carbonates, where several different types of structures are interacting. It is the connectivity both within and between these different structures that controls the porosity-permeability relationship at the larger length scales. Recent advances in machine learning combined with numerical modelling and structural analysis have allowed us to probe the relationship between structure and permeability more deeply. We have used this integrated approach to tackle the challenge of upscaling multimodal and multiscale porous media. We present a novel method for upscaling multimodal porosity-permeability relationships using machine learning based multivariate structural regression. A m-CT image of limestone was divided into sub-volumes and permeability was computed using the DBS model. The porosity-permeability relationship from Menke et al. was used to assign permeability values to the microporosity. Structural attributes of each sub-volume were extracted and then regressed against the solved permeability using an Extra-Trees regression model to derive an upscaled porosity-permeability relationship. Ten upscaled test cases were then modelled at the Darcy scale using the regression and benchmarked against full DBS simulations, a numerically upscaled Darcy model, and a K-C fit. We found good agreement between the full DBS simulations and both the numerical and machine learning upscaled models while the K-C model was a poor predictor in all cases.

A new class of asynchronous discrete-event simulation schemes for advection-diffusion-reaction equations are introduced, which is based on the principle of allowing quanta of mass to pass through faces of a Cartesian finite volume grid. The timescales of these events are linked to the flux on the the face, and the schemes are self-adaptive, local in time and space. Experiments are performed on realistic physical systems related to porous media flow applications, including a large 3D advection diffusion equation and advection diffusion reaction systems. The results are compared to highly accurate results where the temporal evolution is computed with exponential integrator schemes using the same finite volume discretisation. This allows a reliable estimation of the solution error. Our results indicate a first order convergence of the error as a control parameter is decreased.