Sojwal Manoorkar | Searcharxiv

arXiv2025

Pore-scale imaging of hydrogen and methane storage in fractured aquifer rock: The impact of gas type on relative permeability

Underground hydrogen storage in saline aquifers is a potential solution for seasonal renewable energy storage. Among potential storage sites, facilities used for underground natural gas storage have advantages, including well-characterized cyclical injection-withdrawal behavior and partially reusable infrastructure. However, the differences between hydrogen-brine and natural gas-brine flow, particularly through fractures in the reservoir and the sealing caprock, remain unclear due to the complexity of two-phase flow. Therefore, we investigate fracture relative permeability for hydrogen versus methane (natural gas) and nitrogen (commonly used in laboratories). Steady-state relative permeability experiments were conducted at 10 MPa on fractured carbonate rock from the Loenhout natural gas storage in Belgium, where gas flows through {\textmu}m-to-mm scale fractures. Our results reveal that the hydrogen exhibits similar relative permeability curves to methane, but both are significantly lower than those measured for nitrogen. This implies that nitrogen cannot reliably serve as a proxy for hydrogen at typical reservoir pressures. The low relative permeabilities for hydrogen and methane indicate strong fluid phase interference, which traditional relative permeability models fail to capture. This is supported by our observation of periodic pressure fluctuations associated with intermittent fluid connectivity for hydrogen and methane. In conclusion, our findings suggest that the fundamental flow properties of fractured rocks are complex but relatively similar for hydrogen and natural gas. This is an important insight for predictive modeling of the conversion of Loenhout and similar natural gas storage facilities, which is crucial to evaluate their hydrogen storage efficiency and integrity.

physics.flu-dyn↗

arXiv2022

Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling

Field-of-view and resolution trade-offs in X-Ray micro-computed tomography (micro-CT) imaging limit the characterization, analysis and model development of multi-scale porous systems. To this end, we developed an applied methodology utilising deep learning to enhance low resolution images over large sample sizes and create multi-scale models capable of accurately simulating experimental fluid dynamics from the pore (microns) to continuum (centimetres) scale. We develop a 3D Enhanced Deep Super Resolution (EDSR) convolutional neural network to create super resolution (SR) images from low resolution images, which alleviates common micro-CT hardware/reconstruction defects in high-resolution (HR) images. When paired with pore-network simulations and parallel computation, we can create large 3D continuum-scale models with spatially varying flow & material properties. We quantitatively validate the workflow at various scales using direct HR/SR image similarity, pore-scale material/flow simulations and continuum scale multiphase flow experiments (drainage immiscible flow pressures and 3D fluid volume fractions). The SR images and models are comparable to the HR ground truth, and generally accurate to within experimental uncertainty at the continuum scale across a range of flow rates. They are found to be significantly more accurate than their LR counterparts, especially in cases where a wide distribution of pore-sizes are encountered. The applied methodology opens up the possibility to image, model and analyse truly multi-scale heterogeneous systems that are otherwise intractable.

physics.geo-ph↗