AI/machine learning
After years of market shocks, technological breakthroughs, and rising uncertainty, ATCE 2026 will provide new insights on how industry leaders and technical experts are preparing for the next era of the upstream business.
This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
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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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.
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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.
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Machine learning is refining gas lift production optimization with scalable automated workflow.
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The Permian’s produced-water challenge presents an opportunity for innovation to pave the way toward a more sustainable future for the industry.
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The chief operating officer of Chesapeake Energy tells the Unconventional Resources Technology Conference that small wins can pave the path to big achievements.
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The Norwegian major agrees to use Seeq’s software in an effort to maximize production and enhance efficiency across its assets.
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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.
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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.
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This study proposes a hybrid model that combines the capacitance/resistance model, a machine-learning model, and an oil model to assess and optimize water-alternating-gas (WAG) injectors in a carbonate field.
