Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …

This review presents a State-of-Art of geochemical, geomechanical, and hydrodynamic
modelling studies in the Underground Hydrogen Storage (UHS) domain. Geochemical …

Interfacial tension (IFT) is a key physical property that affects various processes in the oil and
gas industry, such as enhanced oil recovery, multiphase flow, and emulsion stability …

Hydrogen, as a clean and efficient energy source, is important in achieving zero-CO 2
targets. This paper explores the potential of hydrogen geologic storage (HGS) in China for …

This study explores the feasibility of utilizing bedded salt deposits as sites for underground
hydrogen storage. We introduce an innovative artificial intelligence framework that applies …

Particle-reinforced metal matrix composites (MMCs) are highly sought after for various
applications due to their robust mechanical properties containing high strength and high …

This study aims to identify the key predictors of the multidimensional energy poverty index
(MEPI) by employing advanced machine learning (ML) ensemble methods. Traditional …

Maximizing the use of explosives is crucial for optimising blasting operations, significantly
influencing productivity and cost-effectiveness in mining activities. This work explores the …

Support vector machines, in the field of machine learning, are supervised learning models
that examine data for classification and regression using learning methods that are …

Cementing operations consists of placing cement slurries between casing-casing and
casing-formation to achieve multiple objectives including zonal isolation, corrosion …