drilling data

This paper presents a multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques to deliver consistently high performance in a scalable manner for sliding.

This paper proposes a novel approach toward drilling maximum-reservoir-contact wells by integrating automated drilling and geosteering software to control the downhole bottomhole assembly, thereby minimizing the need for human intervention.

The capability of modern wellbore surveying to increase asset value through improved subsurface modeling and completion-equipment placement is being overlooked by managers of operating companies.

This paper presents the development of a digital tool for automatically analyzing the readiness of a borehole to accept casing. The tool integrates data-driven and physics-based models to indicate locations of risk along the wellbore.

Smackover Lithium, an Equinor joint venture, will drill new wells on the Franklin project to get a better understanding of its brine makeup and lithium potential.

The company’s new Retina imaging system creates high-resolution borehole images at the drill bit.

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.

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

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.