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

arXiv: https://arxiv.org/abs/2007.02820 · HTML: https://mlsanity.com/paper/2007.02820/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Point-Cloud Deep Learning of Porous Media for Permeability Prediction](https://mlsanity.com/paper/2107.14038/) (2021)
- [Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS](https://mlsanity.com/paper/2109.01818/) (2021)
- [Self-Normalized Density Map (SNDM) for Counting Microbiological Objects](https://mlsanity.com/paper/2203.09474/) (2022)
- [Estimating relative diffusion from 3D micro-CT images using CNNs](https://mlsanity.com/paper/2208.03337/) (2022)
- [Deep learning for diffusion in porous media](https://mlsanity.com/paper/2304.02104/) (2023)
- [A Deep-Learning Iterative Stacked Approach for Prediction of Reactive Dissolution in Porous Media](https://mlsanity.com/paper/2503.08410/) (2025)
- [Vision Mamba for Permeability Prediction of Porous Media](https://mlsanity.com/paper/2510.14516/) (2025)
- [Deep Convolutional Neural Networks Predict Elasticity Tensors and their Bounds in Homogenization](https://mlsanity.com/paper/2109.03020/) (2021)
- [Bayesian Reasoning for Physics Informed Neural Networks](https://mlsanity.com/paper/2308.13222/) (2023)
- [Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data](https://mlsanity.com/paper/2508.00996/) (2025)
- [Neural Networks for Predicting Permeability Tensors of 2D Porous Media: Comparison of Convolution- and Transformer-based Architectures](https://mlsanity.com/paper/2512.01517/) (2025)
- [Topological Data Analysis combined with Machine Learning for Predicting Permeability of Porous Media](https://mlsanity.com/paper/2605.17581/) (2026)

## Abstract (arXiv)

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 ∈ (0.37,0.99) which covers five orders of magnitude span for permeability k ∈ (0.78, 2.1× 105) and tortuosity T ∈ (1.03,2.74).