ORCID Profile
0000-0003-0951-4596
Current Organisation
University of Tokyo
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Publisher: MDPI AG
Date: 07-09-2023
DOI: 10.3390/SU151813447
Publisher: Wiley
Date: 22-07-2022
Publisher: American Geophysical Union (AGU)
Date: 04-2014
DOI: 10.1002/2013GC005154
Publisher: Springer Japan
Date: 08-11-2014
Publisher: Springer Japan
Date: 08-11-2014
Publisher: Wiley
Date: 23-08-2007
Publisher: American Geophysical Union (AGU)
Date: 27-02-2023
DOI: 10.1029/2022WR033267
Abstract: This study presents a workflow to predict the upscaled absolute permeability of the rock core direct from CT images whose resolution is not sufficient to allow direct pore‐scale permeability computation. This workflow exploits the deep learning technique with the data of raw CT images of rocks and their corresponding permeability value obtained by performing flow simulation on high‐resolution CT images. The permeability map of a much larger region in the rock core is predicted by the trained neural network. Finally, the upscaled permeability of the entire rock core is calculated by the Darcy flow solver, and the results showed a good agreement with the experiment data. This proposed deep learning based upscaling method allows estimating the permeability of large‐scale core s les while preserving the effects of fine‐scale pore structure variations due to the local heterogeneity.
No related grants have been discovered for Takeshi Tsuji.