machine learning

Privacy concerns about AI systems are growing. So researchers are testing whether they can remove sensitive data without retraining the system from scratch.

One of the major characteristics of petroleum data analytics is its incorporation of explainable artificial intelligence (XAI). Predictive models of petroleum data analytics are not represented through unexplainable black-box behavior. Predictive models of petroleum data analytics are reasonably explainable. This second part of a two-part series presents the use of XA…

Earlier this year, 19 teams competed in a machine-learning contest held by the Data Analytics Study Group of SPE’s Gulf Coast Section. The was the first competition of its kind for SPE. Here, the organizers of the contest present some of the techniques used and lessons learned from the Machine Learning Challenge 2021.

Physics-based simulations plus machine-learning exercises are yielding a more comprehensive look at production volumes from unconventional assets.

Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, and sports. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface en…

The authors develop an innovative machine-learning method to determine salt structures directly from gravity data.

The work and the provided methodology provide a significant improvement in facies classification.

Phase 1 covers the modeling and monitoring of assets for six ADNOC Group companies. The four phases of the project are expected to be completed by 2022.

A new study confirms the success of a natural-gas leak-detection tool pioneered by Los Alamos National Laboratory scientists that uses sensors and machine learning to locate leak points at oil and gas fields, promising new automatic, affordable sampling across a vast natural gas infrastructure.

The complete paper discusses the successful application of a data-driven approach to analyze production data and identify root causes of slugging in a subsea production system on the Norwegian Continental Shelf.