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. Document Type: Original article Cited as: Ye, S., Song, X., Ma, Z., Gao, Y., Zhu, L., Zhou, M., Xiao, L., Wen, G., Bijeljic, B., Blunt, M. J. A noise-resistant and annotation-free supervoxel-based algorithm for rapid segmentation of multiphase X-ray images. Advances in Geo-Energy Research, 2025, 16(1): 50-59. https://doi.org/10.46690/ager.2025.04.06

Reliable segmentation of multiphase pore‑scale X‑ray images of rocks is necessary to quantify fluid saturation, connectivity, and interfacial geometry. However, current three‑dimensional segmentation methods are typically dataset‑specific, requiring retraining or extensive fine‑tuning whenever rock type, fluid pattern, scanner, or acquisition conditions change. Foundational models such as the Segment Anything Model provide strong two‑dimensional boundary priors, but they are not directly applicable to three‑dimensional data. This study presents SAMamba3D, a parameter‑efficient framework that adapts a largely frozen segment anything model encoder to generalizable three‑dimensional pore‑scale segmentation by coupling it with Mamba‑based volumetric context modeling and progressive cross‑scale feature interaction. For sandstone and carbonate datasets, with different fluids, wettability, and scanning conditions, SAMamba3D matches or outperforms current three‑dimensional baselines while reducing the need for case‑specific retraining. The resulting segmented images preserve physically meaningful descriptors, including fluid saturation, connectivity, and interface morphology, enabling more reliable and rapid analysis of large three‑dimensional multiphase images. Document Type: Original article Cited as: Zhang, R., Song, X., Zhu, L., Bijeljic, B., Li, G., Blunt, M. J. SAMamba3D: Adapting segment anything for generalizable three‑dimensional segmentation of multiphase pore‑scale images. Advances in Geo‑Energy Research, 2026, 21(2): 109‑124. https://doi.org/10.46690/ager.2026.08.03

Artificial intelligence is changing how we observe and predict subsurface flow. Pore‑scale research made this shift visible first: segmentation, generative reconstruction, super‑resolution and rapid property prediction are making the analysis and physical interpretation of large datasets easier, faster and more accurate. In parallel, rapid predictions for CO₂ storage, underground hydrogen storage and hydrocarbon recovery continue to advance. However, understanding flow in the subsurface is inherently a multi scale process both in space ‑ from micron‑sized pores that control displacement processes to carbon dioxide storage sites extending over tens of kilometres ‑ and time ‑ from millisecond displacement to secure storage over millennia. To address this challenge with machine learning tools demands transferability, physical consistency and transparent provenance. In this editorial we explore the current state‑of‑the‑art and provide a personal perspective for the future of artificial intelligence for flow in porous media for the energy transition. Document Type: Editorial Cited as: Zhu, L., Cai, J., Blunt, M. J. Artificial intelligence for multiscale subsurface flow in the energy transition. Advances in Geo‑Energy Research, 2026, 21(1): 98‑100. https://doi.org/10.46690/ager.2026.07.10