Upscaling Permeability Using Multiscale X‐Ray‐CT Images With Digital Rock Modeling and Deep Learning Techniques

作者：Jiang Fei; Guo Yaotian; Tsuji Takeshi; Kato Yoshitake; Shimokawara Mai; Esteban Lionel; Seyyedi Mojtaba; Pervukhina Marina; Lebedev Maxim; Kitamura Ryuta

来源：Water Resources Research, 2023, 59(3).

DOI：10.1029/2022wr033267

• porous flow
• upscaling
• permeability
• deep learning

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 samples while preserving the effects of fine‐scale pore structure variations due to the local heterogeneity.(#br)Key Points(#br)A workflow is proposed to estimate the upscaled absolute permeability of the rock core direct from CT images(#br)A deep learning technique is adopted to establish correlations between high‐resolution computed permeability and low‐resolution images(#br)The upscaling method allows estimating the large‐scale permeability while preserving the effects of fine‐scale permeability variations