DSDE: In Theory

Regional pore-pressure variations in the Leonardian- and Wolfcampian-age producing strata in the Midland and Delaware basins are studied using a variety of subsurface data.

The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.

A numerical simulation study based on experimental data of 2D and 3D models is presented to examine immiscible fingering during field-scale polymer-enhanced oil recovery.

This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.

Given the diversity of coiled tubing well-intervention data, many acquisition labels are often missing or inaccurate. The authors of this paper present a multimodal framework that automatically identifies job type and technologies used during an acquisition.

AI can transform our work, but it demands the highest accuracy. Anything less than perfect in oil and gas and other heavy-asset industries is unacceptable.

This paper examines how data-science-driven work practices can result in substantial reductions in methane emissions compared with other leak-detection and -repair methods.

Digitalization and automation of the drilling process drive the need for an interoperability platform in a drilling operation, where a shared definition and method of calculation of the drilling process state is a fundamental element of an infrastructure to enable interoperability at the rigsite.

This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance.

This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.