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 …

Abstract In Chemical Enhanced Oil Recovery (CEOR), various chemicals such as polymer,
surfactant, alkaline, and nanoparticles are injected solely or in combination to mobilize the …

Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean
electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is …

In hydrocarbon reservoir evaluation, lithology is a key characteristic for determination of
storage capacity and rock properties. Lithology is usually predicted from well log data or …

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) …

Digital rock technology is becoming essential in reservoir engineering and petrophysics.
Three-dimensional digital rock reconstruction, image resolution enhancement, image …

DeePore 2 is a deep learning workflow for rapid estimation of a wide range of porous
material properties based on the binarized micro–tomography images. By combining …

Segmentation of 3D micro-Computed Tomographic (μ CT) images of rock samples is
essential for further Digital Rock Physics (DRP) analysis, however, conventional methods …

High-resolution digital rock micro-CT images captured from a wide field of view are essential
for various geosystem engineering and geoscience applications. However, the resolution of …

Mineral and hydrocarbon exploration relies heavily on geological and geotechnical
information extracted from drill cores. Traditional drill-core characterization is based purely …