machine learning

The gap between machine learning research and effective deployment in the oil and gas industry is an alignment challenge between research questions and real decisions, between model design and operational constraints, and between innovation and the people expected to use it.

This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.

This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.

This study reveals how production-induced depletion and geomechanical stress changes influence child-well performance in the Midland Basin, combining coupled simulations and machine learning to guide optimal well spacing, timing, and placement for infill development.

This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.

Switching from continuous circulation to cyclic huff-‘n’-puff operation in enhanced geothermal systems can significantly delay thermal breakthrough, sustain higher production temperatures, and improve long-term economic performance.

This article is the first in a Q&A series from the SPE Methane Emissions Management Technical Section (MEMTS) on methane intelligence and how oil and gas teams translate emissions data into credible decisions and measurable reductions.

Data and impartial viewpoints can help de-risk exploration portfolios and keep resource estimates in check.

Geophysicist Markos Sourial discusses advances in seismic imaging, the challenges of modern data processing, and what they mean for the next wave of subsurface professionals.