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.

Updates about global exploration and production activities and developments.

The accelerating deployment of machine learning and automation is changing the artificial lift landscape. By embedding intelligence into the control loop, operators now can move from reactive decision-making to proactive, continuous optimization.

The experience captured in this paper illustrates the potential of deepwater riserless wireline subsea intervention capability and the fact that it can be expanded beyond hydraulic-only, simple mechanical, and plugging-and-abandonment scopes.

The subsea field is part of the larger Snøhvit development in the Barents Sea.

A 2D computational fluid dynamics model is extended to a 3D submodel and validated to provide detailed information on the state of the standing valve as a function of time to assist in sucker rod pump design and operation.

Nearly 90% of investment since 2019 has gone to replacing lost production, with $570 billion in spending projected for 2025.

The deal between the Republic of the Congo and the Chinese oil and gas company aims to develop the Banga Kayo, Holmoni, and Cayo blocks and raise national oil output to 200,000 B/D by 2030.