Data & Analytics
The US supermajor will use Permian Basin natural gas to support the hyperscaler’s AI business.
Analysis by the energy research firm sees the value of artificial intelligence growing for exploration and production companies, but the company said increased investment will be necessary.
This article from the SPE Robotics and Autonomous Systems Technical Section (RASTS) explores the insights shared at the recent Offshore Technology Conference (OTC) in Houston about autonomous systems and their role in the industry's future.
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The companies also agreed to collaborate on new AI models to unlock further insights from S&P Global Energy’s upstream data. -
As AI drives record heat loads in data centers, immersion liquid cooling is gaining momentum, and energy companies are lining up to support it.
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Artificial intelligence is prompting oil and gas companies to redefine roles, rethink trust, and rework operations, experts said during CERAWeek.
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The gap between machine learning research and effective deployment in the oil and gas industry is an alignment challenge between research questions and real decisions, between model design and operational constraints, and between innovation and the people expected to use it.
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Technology and partnerships remain important, while phased approaches may supplant lengthy appraisal programs, experts said during CERAWeek.
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CERAWeek panelists see AI as a way to leverage data and people in interpreting data for exploration, but a cultural shift at companies may still be needed.
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Two examples from ONGC show how supervised AI-driven automation scaled well modeling across hundreds of offshore wells, saving more than 1,000 engineering hours.
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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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This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.
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This paper demonstrates how the integration of multiphysics downhole imaging with machine-learning techniques provides a significant advance in perforation-erosion analysis.
