DSDE: In Theory

The main goal of this research work was to determine subseismic faults and fracture corridors and their characteristics, including density and orientation, for a Paleocene fractured carbonate reservoir.

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.

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 of this paper present a novel joint-domain full waveform inversion framework optimizing travel-time accuracy in both data and model domains.

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.

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.

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.

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.