  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.
