Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learning

TL;DR

This paper demonstrates that convolutional neural networks can accurately predict key properties of porous media, such as porosity, permeability, and tortuosity, from images, covering a wide range of system configurations.

Contribution

The study introduces a CNN-based approach to predict porous media properties from images, reproducing the relation between tortuosity and porosity and covering extensive parameter ranges.

Findings

CNN predicts porosity, permeability, and tortuosity with good accuracy.

The relation between tortuosity and porosity is reproduced and validated.

The model covers a wide parameter range with five orders of magnitude.

Abstract

Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ($\varphi$), permeability $k$, and tortuosity ($T$). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. It is demonstrated that the CNNs are able to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between $T$ and $\varphi$ has been reproduced and compared with the empirical estimate. The analysis has been performed for the systems with $\varphi \in (0.37,0.99)$ which covers five orders of magnitude span for permeability $k \in (0.78, 2.1\times 10^5)$ and tortuosity $T \in (1.03,2.74)$.