Underground hydrogen storage in porous formations is a promising solution for large-scale energy storage. Understanding hydrogen flow and trapping at the pore-scale is crucial for assessing storage capacity and recovery efficiency. While pore-scale flow visualisation experiments provide realistic insights, they are resource intensive and technically challenging. Pore-network models offer a computationally efficient tool for simulating multiphase flow in porous media and can serve as a valuable complement to pore-scale experiments. However, their accuracy remains a key uncertainty and must be evaluated for future application. This study evaluates the performance of a quasi-static pore-network model by comparing its predictions against three-dimensional pore-scale hydrogen flow visualisation experiments in a homogeneous Bentheimer sandstone and a layered Clashach sandstone. The model was calibrated to match experimental end-state saturations, and its performance was evaluated through comparisons of spatial saturation profiles and pore occupancy. The novelty of this study lies in the direct comparison of hydrogen displacement between pore-scale experimental observations and pore-network model simulations, providing an assessment of model performance under varying degrees of rock heterogeneity relevant to underground hydrogen storage. The pore-network model shows good agreement with experimental observations for the homogeneous rock, particularly during drainage, and is subsequently used to analyse additional scenarios, including cyclic hydrogen injection and withdrawal, and wettability variations. These simulations provide insights into capillary pressure behaviour and residual saturation trends. In contrast, for the heterogeneous and layered Clashach sandstone, the model fails to capture the trapping and fluid redistribution observed experimentally during imbibition, revealing limitations in modelling fine-scale heterogeneity.

Understanding pore-scale hydrogen displacement and trapping is crucial for developing subsurface hydrogen storage facilities. While pore-scale flow visualization experiments provide critical insights, they are complex and re source-intensive. Quasi-static pore-network models (PNMs) offer a faster alternative for simulating multiphase flow. This study uses a widely employed PNM to simulate hydrogen flow in sandstones, comparing results with pore-scale flow visualization experiments at reservoir conditions.

Two sandstone samples were used: homogeneous Bentheimer and heterogeneous Clashach. Pore networks were extracted comprising pores and throats, and hydrogen-water flow was simulated, modelling drainage and imbibition processes. Results were analysed for fluid saturations and pore occupancies.

For the homogeneous rock, the PNM matches experimental results for both drainage and imbibition, enabling simulations of different wettability conditions and multiple injection and production cycles. For the heterogeneous rock, the PNM reasonably predicts the hydrogen flow path during drainage but fails to accurately predict imbibition. This discrepancy highlights the limitations of PNMs in predicting pore-scale flow in complex rocks.

In conclusion, while PNMs offer a computationally efficient means to simulate hydrogen flow, they cannot currently replace experimental observations for complex rocks. Further validation against experimental findings is necessary to refine these models and expand their applicability for underground hydrogen storage.

Subsurface porous rocks hold significant hydrogen (H₂) storage potential to support an H₂-based energy future. Understanding H₂ flow and trapping in subsurface rocks is crucial to reliably evaluate their storage efficiency. In this work, we perform cyclic H₂ flow visualization experiments on a layered rock sample with varying pore and throat sizes. During drainage, H₂ follows a path consisting of large pores and throats, through a low permeability rock layer, substantially reducing H₂ storage capacity. Moreover, due to the rock heterogeneity and depending on the experimental flow strategy, imbibition unexpectedly results in higher H₂ saturation compared to drainage. These results emphasize that small-scale rock heterogeneity, which is often unaccounted for in reservoir-scale models, plays a vital role in H₂ displacement and trapping in subsurface porous media, with implications for efficient storage strategies.

Microporosity is commonly assumed to be non-connected porosity and not commonly studied in geoengineering industry. However, the presence of micropores plays a key role in connecting macropores and it can contribute significantly to the overall flow performance. In this study, targeted CO₂ storage carbonate fields in Southeast Asia have significant amounts of microporosity ranging from 10 to 60% of the total measured porosity. Microporosity can only be seen in high resolution images. To study the unresolved and the resolved microporosity, Middle Miocene carbonate samples from CO₂ storage candidate fields were scanned using lower resolution micro-computed micro-tomography (micro-CT) and higher resolution synchrotron light source to understand the pore scale structure of the carbonate sample at different length scales. This paper proposes a proof-of-concept upscaling method that integrates multiscale 3D imaging techniques and trendline analysis to establish porosity-permeability relationships with microporosity insight. After image acquisition and processing, the images were divided into smaller sub-volumes. Pore-scale modelling was conducted to predict the permeability using Darcy-Brinkman-Stokes (DBS) model. Then, a nano-scale porosity-permeability transform is generated using natural log trendline fitting based on simulation results. The porosity-permeability transform is further extended to three cases to cover the low case, mid case, and high case of datapoint fittings and is further validated with laboratory measured data. The established porosity-permeability transforms in this study have been applied to compare with petrophysical derived porosity-permeability transforms with better performance (higher R² value) for low permeability datapoint. The multiscale imaging upscaling workflow has integrated machine learning during image segmentation with pore-scale modelling and trendline fitting during the upscaling study. It emphasises the importance of seeing the unseen (unresolved microporous phase) to understand the internal texture and microstructure of a rock sample in order to understand the connectivity of the overall flow performance in a carbonate rock.

The traditional model of solid dissolution in porous media consists of three dissolution regimes (uniform, compact, wormhole)-or patterns-that are established depending on the relative dominance of reaction rate, flow, and diffusion. In this work, we investigate the evolution of pore structure using numerical simulations during acid injection on two models of increasing complexity. We investigate the boundaries between dissolution regimes and characterize the existence of a fourth dissolution regime called channeling, where initially 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 suggests that the traditional conceptual model of dissolution regimes must be updated to incorporate the channeling regime for reliable forecasting of dissolution in applications like geothermal energy production and geologic carbon storage.