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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.26198 |
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| _version_ | 1866909878101999616 |
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| author | Guo, Yaotian Jiang, Fei Tsuji, Takeshi Kato, Yoshitake Shimokawara, Mai Esteban, Lionel Seyyedi, Mojtaba Pervukhina, Marina Lebedev, Maxim Kitamura, Ryuta |
| author_facet | Guo, Yaotian Jiang, Fei Tsuji, Takeshi Kato, Yoshitake Shimokawara, Mai Esteban, Lionel Seyyedi, Mojtaba Pervukhina, Marina Lebedev, Maxim Kitamura, Ryuta |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26198 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning-based upscaling of rock permeability from pore scale to core scale: effect of training dataset size and sub-core volumes Guo, Yaotian Jiang, Fei Tsuji, Takeshi Kato, Yoshitake Shimokawara, Mai Esteban, Lionel Seyyedi, Mojtaba Pervukhina, Marina Lebedev, Maxim Kitamura, Ryuta Geophysics 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. |
| title | Machine learning-based upscaling of rock permeability from pore scale to core scale: effect of training dataset size and sub-core volumes |
| topic | Geophysics |
| url | https://arxiv.org/abs/2510.26198 |