machine learning

This paper proposes a novel, real-time pump failure prediction method using machine learning with scaled load ratio to accurately predict pump failures using only surface pump load data.

Aramco says it has saved $770 million over the past 3 years from the $70 million it has invested over the same period in corrosion management technologies.

The deal adds physics-based reservoir modeling and real-time decision workflows to SLB’s digital portfolio.

This study explores the use of autoencoder models with convolutional neural networks to present a framework and prototype for early and accurate kick detection during offshore oilwell drilling.

A discussion at the inaugural executive breakfast convened by the SPE Data Science and Engineering Analytics Technical Section, held alongside CERAWeek by S&P Global and powered by Black & Veatch, tackled the challenge of value creation from artificial intelligence in the energy industry.

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.