In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …

Mineral prospectivity map** (MPM) aims to reduce the areas for searching of mineral
deposits. Various statistical models that have been successfully adopted to delineate …

Similar to many fields of sciences, recent deep learning advances have been applied
extensively in geosciences for both small-and large-scale problems. However, the necessity …

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 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 …

Image segmentation remains the most critical step in Digital Rock Physics (DRP) workflows,
affecting the analysis of physical rock properties. Conventional segmentation techniques …

Permeability prediction of porous media from numerical approaches is an important
supplement for experimental measurements with the benefits of being more economical and …

Digital rock imaging plays an important role in studying the microstructure and macroscopic
properties of rocks, where microcomputed tomography (MCT) is widely used. Due to the …

This study introduces an integrated approach for lithofacies classification utilizing core
samples, wireline logs, and a machine learning technique. It is specialized for the Eagle …

Coalbed methane (CBM) is an important unconventional fuel source, and its efficient
extraction is of great significance in reducing greenhouse gas emissions, energy …