In the past several decades, machine learning has gained increasing interest in the oil and
gas industry. This paper presents a comprehensive review of machine learning studies for …

The primary task of machine learning is to extract valuable information from the data that is
generated every day, process it to learn from it, and take useful actions. Original language …

To overcome the deficiencies of current porosity prediction methods, the XGBoost algorithm
is introduced to construct a model for porosity prediction, and the obtained model is …

Surprisingly, no investigation has been explored relationships between operating variables
and metallurgical responses of coal column flotation (CF) circuits based on industrial …

Nowadays, it is commonplace for geological surveys to integrate multi-source geophysical
data and drilling data in order to construct three-dimensional (3D) lithological models. In this …

Abstract High-Pressure Grinding Rolls (HPGR), as a modified type of roll crushers, could
intensively reduce the energy consumptions in the mineral processing comminution units …

Coal exploration in the Indian scenario is challenging due to plenty of carbon contents and
dirt bands within a coal seam. Manual interpretation of geophysical logging data in such …

The coalbed methane blocks are always structurally and topographically complex, and there
are no models to accurately predict the coalbed methane content in the southern Sichuan …

Numerical models can be used for many purposes in oil and gas engineering, such as
production optimization and forecasting, uncertainty analysis, history matching, and risk …

Reservoir characterization, essential for understanding subsurface heterogeneity, often
faces challenges due to scale-dependent variations. This study addresses this issue by …