Artificial lift
Unlike traditional AI approaches that rely primarily on historical data, physics-based AI integrates physical laws, engineering expertise, and operational constraints directly into the learning process. Three recent SPE papers illustrate how this paradigm is transforming the industry through intelligent condition monitoring, virtual sensing, and autonomous production …
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.
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With confidence in subsea boosting performance growing, BP is incorporating pumping systems into both greenfield developments and brownfield expansions. -
This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
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Honorees Rajan Chokshi and Mike Poythress say the guidance they received, and later shared with others, helped shape decades of innovation and leadership in artificial lift.
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As digital technologies become commonplace, industry leaders say core engineering knowledge remains essential for making informed decisions and avoiding costly mistakes.
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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.
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This article from the SPE Artificial Lift Technical Section highlights a positive reality of our industry, one that centers on connecting and collaborating for success.
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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.
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This paper proposes a novel, real-time pump failure prediction method using machine learning with scaled load ratio to accurately predict pump failures using only surface pump load data.
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Production delivered by the subsea artificial lift system will equal that obtained by drilling two new wells, according to BP.
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Examples demonstrate how an Integrated Operations Center as a Service (IOCaaS) model, powered by artificial intelligence, reduced costs by 5% and increased production by 6% in Canada.
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