Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learning
This work addresses the problem of characterizing porous media properties for researchers in fluid dynamics or materials science, but it is incremental as it applies existing deep learning methods to a specific domain.
The study tackled predicting porosity, permeability, and tortuosity of porous media from images using convolutional neural networks, achieving good accuracy across a wide range of values, including permeability spanning five orders of magnitude.
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 $T \in (1.03,2.74)$.