Presented in this work is a framework for the data-driven determination of multi-scale porous media parametrizations. Simulations of flow and transport in a porous medium at the REV scale, although efficient, require well defined parameters that represent pore-scale phenomena to maintain their accuracy. Determining the optimal parameters for this often require expensive pore-scale calculations. This work outlines a series of four steps where these parameters can be calculated from pore scale data, their solutions generalized with a convolutional neural network, and their content better understood with descriptive pore metrics.

翻译：本文提出了一种数据驱动确定多尺度多孔介质参数化的框架。在REV尺度下进行多孔介质流动与输运模拟虽然高效，但需要明确定义的参数来表征孔隙尺度现象，以维持其精度。确定此类最优参数通常需要昂贵的孔隙尺度计算。本研究概述了一系列四个步骤：从孔隙尺度数据计算这些参数，利用卷积神经网络泛化其解，并通过描述性孔隙度量更好地理解其内容。