Machine learning-based upscaling of rock permeability from pore scale to core scale: Effect of training dataset size and sub-core volumes
- DOI
- 10.1016/j.ees.2025.11.008
- Published
- 03
- Container
- Earth Energy Science
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.ees.2025.11.008,
title = {Machine learning-based upscaling of rock permeability from pore scale to core scale: Effect of training dataset size and sub-core volumes},
author = {Yaotian Guo and Fei Jiang and Takeshi Tsuji and Yoshitake Kato and Mai Shimokawara and Lionel Esteban and Mojtaba Seyyedi and Marina Pervukhina and Maxim Lebedev and Ryuta Kitamura},
year = {2026},
journal = {Earth Energy Science},
doi = {10.1016/j.ees.2025.11.008},
url = {https://doi.org/10.1016/j.ees.2025.11.008}
}RIS
TY - JOUR TI - Machine learning-based upscaling of rock permeability from pore scale to core scale: Effect of training dataset size and sub-core volumes AU - Yaotian Guo AU - Fei Jiang AU - Takeshi Tsuji AU - Yoshitake Kato AU - Mai Shimokawara AU - Lionel Esteban AU - Mojtaba Seyyedi AU - Marina Pervukhina AU - Maxim Lebedev AU - Ryuta Kitamura PY - 2026 JO - Earth Energy Science DO - 10.1016/j.ees.2025.11.008 UR - https://doi.org/10.1016/j.ees.2025.11.008 ER -
APA
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. https://doi.org/10.1016/j.ees.2025.11.008
Source records
- doaj · retrieved 2026-09-27T09:27:23.205Z