Building Trust in AI/ML Solutions: Key Factors for Successful Adoption in Drilling Optimization and Hazard Prevention

An examination of the factors — performance, infrastructure, transparency and trust — that determine whether AI/ML drilling solutions are successfully adopted in the field.

SPE-218455-MS — SPE Norway Subsurface Conference, Bergen, Norway, April 17, 2024

S. Schaefer (Exebenus); O. Revheim (Exebenus)

DOI: 10.2118/218455-MS

Abstract

The use of AI/ML technologies has provided breakthrough performance in automated predictive data analytics, and data-driven solutions increasingly define the future of drilling optimization and hazard prevention. This paper examines why successful adoption of these technologies in the operational environment depends on more than model accuracy alone. Drawing on project experience from various parts of the world, it identifies the prerequisites for building trust in AI/ML solutions — including high-performing technology, mature IT infrastructure and support services, transparent and interpretable outputs, continuous monitoring, and close collaboration between technology providers and operational teams — and discusses how these factors combine to enable digital transformation and confident, scalable deployment.