AI/machine learning
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In oil and gas operations, every decision counts. For more than 2 decades, SiteCom has been the trusted digital backbone for well operations worldwide, driving insight, collaboration, and efficiency.
This study presents a novel hybrid approach to enhance fraud detection in scanned financial documents.
This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.
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The RoboWell technology for well control will be available globally through Halliburton’s Landmark iEnergy hybrid cloud. -
Automated workflow unifies geological, completion, and production data to inform speedier, better investment decisions for nonoperated assets.
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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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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.
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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.
