Data & Analytics
AI‑driven data center growth is straining US power grids and accelerating interest in enhanced geothermal systems as a scalable, low‑carbon solution.
Drilling experts recently shared candid views on what will be required for their segment of the upstream business to move to the next stage of development.
This month’s column highlights how artificial intelligence is influencing SPE programming, publications, and new tools, while also transforming day‑to‑day operations across our industry. The column explores energy supply implications and practical field applications, showing how SPE is helping members turn AI into a tool for progress.
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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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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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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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This paper presents a workflow that leverages a multiagent conversational system to integrate data, analytics, and domain expertise for improved completion strategies.
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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.
