Hydrogen withdrawal from subsurface porous formations is expected to experience fast pressure decline rates, yet the pore-scale effects remain poorly explored. This study examines how pressure decline and simultaneous brine influx affect the withdrawal of stored hydrogen in a water-wet Bentheimer sandstone, representing hydrogen storage in saline aquifers. Brine was injected while the outlet pressure was reduced at a fixed rate. Two initial conditions were tested: A high gas saturation, representative of regions above the gas-water contact, and a residual gas saturation, representative of regions below it. Micro-computed tomography was used to quantify gas distribution, connectivity, and the dominant displacement mechanism during pressure decline with continued brine influx. The observations show that capillary pressure can increase during pressure decline, showing that the main displacement mechanism is drainage, even as brine is flowing. The gas saturation increased through the expansion of trapped gases, and large gas clusters connected to the outlet and were produced by expansion. No imbibition displacement was seen despite the high gas saturation reached by expansion. When pressure decline began from residual conditions, the gas saturation increase was proportional to the magnitude of pressure decline, whereas starting from a high gas saturation led to larger residual clusters and greater connectivity. These observations suggest that under continuous pressure decline, local capillary pressure can increase, preventing imbibition displacement of gas by water. This makes the interpretation of laboratory experiments to find the critical gas saturation challenging, as it depends on the displacement process. Gas production occurs primarily through expansion-driven drainage rather than through normal displacement.

This study introduces a three-dimensional supervoxel segmentation method to accurately separate solid and fluid phases in X-ray images of porous materials, with applications in energy research. Compared with intelligent segmentation algorithms requiring model training, the proposed method operates as a ready-to-use solution with significantly enhanced efficiency. When benchmarked against conventional approaches such as watershed transformation, our technique demonstrates superior segmentation accuracy. Tested on porous rock and gas diffusion layers under varying wettability, it accurately quantifies fluid saturation, interfacial area, curvature, and contact angles-key parameters for enhanced oil recovery, CO₂ storage, and hydrogen fuel cells. The proposed three-dimensional segmentation method is noise-resistant and annotation-free, improving both the accuracy and efficiency of segmenting diverse micro-structural material datasets and providing reliable measurements of their geometric characteristics.