Driving digital rock towards machine learning: Predicting permeability with gradient boosting and deep neural networks

We present a research study aimed at testing of applicability of machine learn-ing techniques for permeability prediction. We prepare a training set containing. 3D scans of Berea sandstone subsamples 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 pre-diction. We compare predictive power of various descriptors and methods. The; latter include Gradient Boosting, Deep Neural Networks (DNN) and Convo-lutional Neural Networks (CNN). Introduced Deep Learning-based descriptors; outperform previously used alternatives. 3D CNN outperforms the competitors in terms of the percent error and prediction time. The results demonstrate the applicability of machine learning for image-based permeability prediction and open a new area of Digital Rock research.