Author Identifier (ORCID)
Maxim Lebedev: https://orcid.org/0000-0003-1369-5844
Abstract
Permeability characterizes the capacity of porous formations to conduct fluids, thereby governing the performance of carbon capture, utilization, and storage (CCUS), hydrocarbon extraction, and subsurface energy storage. A reliable assessment of rock permeability is therefore essential for these applications. Direct estimation of permeability from low-resolution CT images of large rock samples offers a rapid approach to obtain permeability data. However, the limited resolution fails to capture detailed pore-scale structural features, resulting in low prediction accuracy. To address this limitation, we propose a convolutional neural network (CNN)-based upscaling method that integrates high-precision pore-scale permeability information into core-scale, low-resolution CT images. In our workflow, the large core sample is partitioned into sub-core volumes, whose permeabilities are predicted using CNNs. The upscaled permeability at the core scale is then determined through a Darcy flow solver based on the predicted sub-core permeability map. Additionally, we examine the optimal sub-core volume size that balances computational efficiency and prediction accuracy. This framework effectively incorporates small-scale heterogeneity, enabling accurate permeability upscaling from micrometer-scale pores to centimeter-scale cores.
Keywords
Digital rock physics, machine learning, permeability upscaling
Document Type
Journal Article
Date of Publication
3-1-2026
Volume
2
Issue
1
Publication Title
Earth Energy Science
Publisher
Elsevier
School
School of Engineering
Funders
Japan Society for the Promotion of Science (JP22K03927, JP24H00440, JP21H05202, JPMJSP2111)
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Guo, Y., Jiang, F., Tsuji, T., Kato, Y., Shimokawara, M., Esteban, L., Seyyedi, M., Pervukhina, M., Lebedev, M., & Kitamura, R. (2026). Machine learning-based upscaling of rock permeability from pore scale to core scale: Effect of training dataset size and sub-core volumes. Earth Energy Science, 2(1), 100041. https://doi.org/10.1016/j.ees.2025.11.008