Data & Analytics
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Effective data analysis depends on seeing connections that matter. Explore how Spotfire enables a more intuitive approach to understanding petroleum data.
Schneider Electric says the deal advances its vision of creating intelligent industrial ecosystems that connect physical assets with digital insights across the asset life cycle.
With the latest addition, the Italian major’s computational capacity passes the exaflop threshold, making the firm the world’s leading company by computing power in the new TOP500 global ranking.
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Artificial intelligence is prompting oil and gas companies to redefine roles, rethink trust, and rework operations, experts said during CERAWeek. -
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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Data centers could add up to 6 Bcf/D of US gas demand by 2030, creating a new opportunity for producers and reshaping how oil companies think about electricity supply.
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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.
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This paper presents a workflow that leverages a multiagent conversational system to integrate data, analytics, and domain expertise for improved completion strategies.
