Estimating relative diffusion from 3D micro-CT images using CNNs

TL;DR

This paper demonstrates that CNNs can accurately predict relative diffusion directly from 3D pore geometries, effectively combining morphological models with deep learning to handle complex partially saturated porous media.

Contribution

It introduces a CNN-based approach for predicting relative diffusion from full pore geometries, addressing challenges in partially saturated media.

Findings

CNNs accurately predict relative diffusion in porous media.

The method effectively handles complex geometries in partial saturation.

Prediction reduces computation time compared to classical methods.

Abstract

In the past several years, convolutional neural networks (CNNs) have proven their capability to predict characteristic quantities in porous media research directly from pore-space geometries. Due to the frequently observed significant reduction in computation time in comparison to classical computational methods, bulk parameter prediction via CNNs is especially compelling, e.g. for effective diffusion. While the current literature is mainly focused on fully saturated porous media, the partially saturated case is also of high interest. Due to the qualitatively different and more complex geometries of the domain available for diffusive transport present in this case, standard CNNs tend to lose robustness and accuracy with lower saturation rates. In this paper, we demonstrate the ability of CNNs to perform predictions of relative diffusion directly from full pore-space geometries. As such, our CNN conveniently fuses diffusion prediction and a well-established morphological model which describes phase distributions in partially saturated porous media.