Data & Analytics
The deal adds physics-based reservoir modeling and real-time decision workflows to SLB’s digital portfolio.
The paper presents the design and successful field deployment of the first closed-loop hydraulic fracturing program.
Working with Dell Technologies and NVIDIA, the French supermajor is targeting improved seismic processing and artificial intelligence applications.
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EQT is benchmarking its way to basin-leading productivity and relying on partnerships and new technology to turn KPIs into operational reality. -
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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The companies also agreed to collaborate on new AI models to unlock further insights from S&P Global Energy’s upstream data.
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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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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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The authors propose a deep-learning-based approach enabling near-real-time CO2-plume visualization and rapid data assimilation incorporating multiple geological realizations for predicting future CO2 plume evolution and area-of-review determination.
