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

TL;DR

This study explores machine learning models, including Gradient Boosting and Deep Neural Networks, for predicting rock permeability from 3D imaging data, demonstrating promising results for digital rock analysis.

Contribution

It introduces a novel application of machine learning techniques to predict permeability from digitized rock images, comparing different feature sets and models.

Findings

Machine learning effectively predicts permeability from 3D rock images.

Deep learning models outperform traditional feature-based methods.

The approach opens new avenues for digital rock research.

Abstract

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.