machine learning

A new report from GlobalData provides an overview of the digitalization efforts within the industry and their potential to transform operations.

This study examines the implementation of a predictive maintenance method using artificial intelligence and machine learning for offshore rotating production-critical equipment. Conducted over 2 years at Murphy Oil’s deepwater platforms in the Gulf of Mexico, the project aimed to detect equipment issues early, reduce downtime, and streamline maintenance processes.

This selection of cutting-edge articles spotlights how experimental concepts are now driving cost-saving strategies in unconventional development. It’s a reminder that innovation often comes from creative thinking, not just new tools or tech partnerships.

The USGS has said up to 19 million tons of lithium resource is contained in the briny waters of the Smackover formation in Arkansas.

Technology uptake aimed at optimizing resources, delivering consistency, and augmenting what humans can do.

Machine learning and a decade of gas composition records helped the operator identify wells that were most likely to produce paraffins.

Experts at SPE’s Annual Technical Conference and Exhibition say that despite AI’s great potential, it’s important to be realistic about AI’s capabilities and to remember that successful projects solve specific business problems.

New and evolving artificial lift technology is helping operators improve production rates.

This paper presents a novel modeling framework for predicting residual oil saturation in carbonate rocks. The proposed framework uses supervised machine learning models trained on data generated by pore-scale simulations and aims to supplement conventional coreflooding tests or serve as a tool for rapid residual oil saturation evaluation of a reservoir.

Energy efficiency is crucial for the oil and gas industry, where operational costs and environmental impact are under constant scrutiny. Predicting and managing electrical consumption and peak demand accurately, especially with the variability of weather conditions, is a significant challenge. This work presents a neural network model trained on historical weather and…