Seeing Permeability From Images: Fast Prediction with Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based framework for rapid permeability prediction from images of porous media, significantly reducing computational time and outperforming traditional methods, especially for heterogeneous pore structures.

Contribution

The study presents a novel image-based permeability prediction method using CNNs, incorporating physical parameters for improved accuracy over conventional CNNs.

Findings

CNN achieves high accuracy across diverse pore geometries.

Computational time is reduced by several orders of magnitude.

Physics-informed CNN outperforms regular CNN in heterogeneous media.

Abstract

Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples, (2) computation of permeability via fluid dynamics simulations, (3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially for those with dilated pores. Owning to such heterogeneity, the permeability cannot be estimated using the conventional Kozeny-Carman approach. Computational time was reduced by several orders of magnitude compared to fluid dynamic simulations. We found that, by including physical parameters that are known to affect permeability into the neural network, the physics-informed CNN generated better results than regular CNN, however improvements vary with implemented heterogeneity.