# Driving Digital Rock towards Machine Learning: predicting permeability with Gradient Boosting and Deep Neural Networks

arXiv: https://arxiv.org/abs/1803.00758 · HTML: https://mlsanity.com/paper/1803.00758/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph](https://mlsanity.com/paper/2104.05608/) (2021)
- [Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS](https://mlsanity.com/paper/2109.01818/) (2021)
- [Estimating relative diffusion from 3D micro-CT images using CNNs](https://mlsanity.com/paper/2208.03337/) (2022)
- [Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data](https://mlsanity.com/paper/2502.01634/) (2025)

## Abstract (arXiv)

We present a research study aimed at testing of applicability of machine learning techniques for prediction of permeability of digitized rock samples. We prepare a training set containing 3D images of sandstone samples imaged with X-ray microtomography and corresponding permeability values simulated with Pore Network approach. We also use Minkowski functionals and Deep Learning-based descriptors of 3D images and 2D slices as input features for predictive model training and prediction. We compare predictive power of various feature sets and methods. The later include Gradient Boosting and various architectures of Deep Neural Networks (DNN). The results demonstrate applicability of machine learning for image-based permeability prediction and open a new area of Digital Rock research.