This study offered a detailed review of data sciences and machine learning (ML) roles in
different petroleum engineering and geosciences segments such as petroleum exploration …

Identification of geological features from seismic data such as faults, salt bodies, and
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …

Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …

Seismic data processing involves techniques to deal with undesired effects that occur during
acquisition and pre-processing. These effects mainly comprise coherent artefacts such as …

Three-dimensional seismic interpretation and property estimation is essential for subsurface
map** and characterization, in which machine learning, particularly supervised …

Seismic velocity inversion plays a vital role in various applied seismology processes. A
series of deep learning methods have been developed that rely purely on manually …

Salt interpretation using seismic data is essential for structural interpretation and oil and gas
exploration. Although deep learning has made great progress in automatic salt image …

Seismic facies map** from a 3D seismic cube is of significant value to various seismic
interpretation and characterization tasks. Traditional facies map** is based on examining …

Structural curvatures are widely used seismic attributes that help interpreters to understand
both structural and stratigraphic features. Traditional structural curvature extractions are …

Underground reservoir information can be obtained through well-log interpretation.
However, some logs might be missing due to various reasons, such as instrument failure. A …