AI/machine learning
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.
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Total is expanding its development and use of artificial intelligence to bolster its exploration work, collaborating with Google Cloud—which is stepping up its presence in the oil and gas industry. -
The goal of cognitive computing is not to eliminate humans but allow highly skilled professionals to spend time doing what’s most valuable for the company.
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A look at how policy, future workforce perception, and industry standards will shape energy companies in the near and distant future.
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How can AI systems incorporate processes mimicking the slower logic- and causality-based reasoning patterns of the left brain?
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Tiny soil samples may contain as many as 300,000 species of microbial life, but a Netherlands-based startup has figured out that between 50 and 200 of them can tell an operator if a drilling location will hold oil and gas reserves.
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Southwest Research Institute is working to improve the accuracy of pipeline leak detection using sensors, artificial intelligence, and deep learning.
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As artificial intelligence makes a significant impact on various industries, an expert examines the roles it could play in streamlining oil and gas operations in the near future.
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A consultant examines the ways in which artificial intelligence and machine learning solutions may have a significant impact on industry operations.
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As the drilling industry improves its efforts to capture drilling operation activities in real time, it has generated a significant amount of data that drilling engineers cannot process on their own.
