Upscaling Permeability Using Multiscale X‐Ray‐CT Images With Digital Rock Modeling and Deep Learning Techniques
Upscaling Permeability Using Multiscale X‐Ray‐CT Images With Digital Rock Modeling and Deep Learning Techniques
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DOI：
10.1029/2022wr033267
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发表时间：
2023-02
影响因子：
5.4
通讯作者：
Fei Jiang;Yaotian Guo;T. Tsuji;Y. Kato;Mai Shimokawara;L. Esteban;M. Seyyedi;M. Pervukhina;M. Lebedev;R. Kitamura
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文献类型：
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作者：
Fei Jiang;Yaotian Guo;T. Tsuji;Y. Kato;Mai Shimokawara;L. Esteban;M. Seyyedi;M. Pervukhina;M. Lebedev;R. Kitamura
本研究提出了一种直接从CT图像预测岩心放大绝对渗透率的工作流程，CT图像的分辨率不足以直接进行孔隙尺度渗透率计算。该工作流程利用深度学习技术，通过对高分辨率CT图像进行流动模拟获得岩石原始CT图像数据及其相应的渗透率值。用训练好的神经网络预测岩心中更大区域的渗透率图。最后，利用达西渗流求解器计算了整个岩心的渗透率，计算结果与实验数据吻合较好。提出的基于深度学习的放大方法允许估计大规模岩心样品的渗透率，同时保留由于局部异质性引起的细尺度孔隙结构变化的影响。
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.