AI/machine learning
The Norwegian major said it is using artificial intelligence for predictive maintenance throughout its facilities and for interpretation of seismic data from the Norwegian continental shelf.
This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.
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.
-
Automated workflow unifies geological, completion, and production data to inform speedier, better investment decisions for nonoperated assets. -
Both new and old vessels are benefiting from automation processes that can improve operational efficiency, predict downtime, and debottleneck workflows using a flurry of crucial data points.
-
The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.
-
Operators tell an audience at the Unconventional Resources Technology Conference how a hybrid expandable liner system and machine-learning-based analysis improve the bottom line.
-
Machine learning is refining gas lift production optimization with scalable automated workflow.
-
The Permian’s produced-water challenge presents an opportunity for innovation to pave the way toward a more sustainable future for the industry.
-
The chief operating officer of Chesapeake Energy tells the Unconventional Resources Technology Conference that small wins can pave the path to big achievements.
-
The Norwegian major agrees to use Seeq’s software in an effort to maximize production and enhance efficiency across its assets.
-
This article explores the implementation of artificial intelligence vision for leak monitoring automation in the oil and gas industry and its role in improving safety standards, operational efficiency, and environmental performance.
-
This paper investigates the use of machine learning to rapidly predict the solutions of a high-fidelity, complex physics model using a simpler physics model.
