gas lift

This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.

A combination of physics principles and machine-learning techniques is used in this work to develop a virtual flowmeter for oil-production optimization in electrical-submersible-pump-lifted oil wells, resulting in reliable generalization.

This paper presents the deployment of an artificial intelligence-enabled autonomous gas lift optimization system, integrating real-time centralized advanced process control with cloud-based analytics to enhance artificial gas lift performance in producer wells.

While equipment run life and reliability remain core concerns, experts say operators are increasingly turning to real-time surveillance and autonomous optimization to unlock new production gains.

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.

This paper presents a robust workflow to identify optimization opportunities in gas lift wells through real-time data analysis and a surveillance-by-exception methodology.

Operators are turning to new gas-lift and nanoparticle-fluid technologies to drive up production rates.

This paper presents a case study highlighting the demonstration, refinement, and implementation of a machine-learning algorithm to optimize multiple electrical-submersible-pump wells in the Permian Basin.

This paper presents a closed-loop iterative well-by-well gas lift optimization workflow deployed to more than 1,300 operator wells in the Permian Basin.

This paper explores the use of machine learning in predicting pump statuses, offering probabilistic assessments for each dynacard, automating real-time analysis, and facilitating early detection of pump damage.