Geophysical logging series are valuable geological data that record the physical and
chemical information of borehole walls and in-situ formations, and are widely used by …

The identification of sweet spots, areas within a reservoir with the highest production
potential, has been revolutionized by the integration of machine learning (ML) algorithms …

Zircon is a widely-used heavy mineral in geochronological and geochemical research
because it can extract important information to understand the history and genesis of rocks …

Lithofacies identification and classification are critical for characterizing the hydrocarbon
potential of unconventional resources. Although extensive applications of machine learning …

Coal structure is closely related to microscopic and macroscopic properties of coal, and its
accurate identification is of great significance to coalbed methane (CBM) reservoir …

The lithology log, an integral component of the master log, graphically portrays the
encountered lithological sequence during drilling operations. In addition to offering real-time …

Subtle sandstone reservoirs are difficult to identify due to their weak seismic responses.
Here, we propose to identify subtle sandstone reservoirs by an unsupervised machine …

The evolution of saltating tracks driven by strong wind remains unknown due to the low
accuracy or recall rates of saltating particle tracking algorithms (SPTs). Manual identification …

Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and
compilation of sedimentary facies. Traditional sedimentary facies analysis is a labor …

As important units for evaluating shale gas reservoirs, lithofacies exert obvious control on
the abundance of shale organic matter and the enrichment degree of gas reservoirs …