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

Abstract

In recent years, convolutional neural networks (CNNs) have demonstrated their effectiveness in predicting bulk parameters, such as effective diffusion, directly from pore-space geometries. CNNs offer significant computational advantages over traditional methods, making them particularly appealing. However, the current literature primarily focuses on fully saturated porous media, while the partially saturated case is also of high interest for various applications. Partially saturated conditions present more complex geometries for diffusive transport, making the prediction task more challenging. Traditional CNNs tend to lose robustness and accuracy with lower saturation rates. In this paper, we overcome this limitation by introducing a CNN, which conveniently fuses diffusion prediction and a well-established morphological model that describes phase distributions in partially saturated porous media. We demonstrate the ability of our CNN to perform accurate predictions of relative diffusion directly from full pore-space geometries. Finally, we compare our predictions with well-established relations such as the one by Millington–Quirk.

Keywords

• Digital rock
• Neural networks
• Deep learning
• Relative diffusion
• Partial saturation