machine learning

This article from the SPE Methane Technical Section features Arvind Ravikumar of the University of Texas at Austin and focuses on how the Energy Emissions Modeling and Data Lab is integrating satellite observations, facility-level measurements, operational data, and emissions inventories into more credible methane accounting for oil and gas systems.

This case study describes how edge computing and industrial internet of things platforms were deployed to automate and optimize production operations across four distinct basins.

Analysis by the energy research firm sees the value of artificial intelligence growing for exploration and production companies, but the company said increased investment will be necessary.

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.