Pore-scale imaging and modeling has advanced greatly through the integration of Deep
Learning into the workflow, from image processing to simulating physical processes. In …

Automated Mineralogy (AM) is a semi-automatic mineralogical tool based on a scanning
electron micrography-energy dispersion spectrometry (SEM‒EDS) platform. It has the …

Segmentation is a critical step in Digital Rock Physics (DRP) as the original images are
available in a gray-scale format. Conventional methods often use thresholding to delineate …

The establishment of digital images of cores with multi-mineral components holds the
premise of analyzing the spatial distribution of shale minerals and carrying out numerical …

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 …

Abstract In this paper, Convolutional Neural Networks (CNNs) are trained to rapidly estimate
several physical properties of porous media using micro-computed tomography (micro-CT) …

Flow, transport, mechanical, and fracture properties of porous media depend on their
morphology and are usually estimated by experimental and/or computational methods. The …

The microstructural evaluation of oil shale is challenging which demands the use of several
complementary methods. In particular, an improved insight into the pore network structure …

In this article, we propose to discretize the problem of linear elastic homogenization by finite
differences on a staggered grid and introduce fast and robust solvers. Our method shares …

The presence of fractures in a rock mass can have a substantial influence on its mechanical
and hydraulic properties. For many years, computed tomography (CT) scan has been …