Data & Analytics
Operational drift in oil and gas starts with small, undocumented deviations that compound over time, making real-time visibility, digital workflows, and frontline execution data critical for preventing safety incidents, compliance failures, and costly operational disruptions.
Discover how AI and machine learning are transforming oil and gas field development by reducing subsurface uncertainty, optimizing development decisions, and maximizing long-term reservoir value from concept selection through production.
As upstream operators move beyond isolated experiments, the hardest part of the AI journey is not building a model, it is making it stick.
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Join us for Part 2 of a four-part series focused on addressing the implementation of AI in the petroleum industry using a real case study. -
The program is designed to analyze, report, and study solutions for oil and gas greenhouse gas emissions.
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Grab a pen and paper and settle in for Part 1 of a four-part series focused on addressing the implementation of AI in the petroleum industry using a real case study.
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Texas A&M is offering a course designed in collaboration with Peloton for students in the petroleum engineering program.
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SPE has established three new technical sections—the Management Technical Section, the Methane Emissions Management Technical Section, and the Data Science & Engineering Analytics Technical Section.
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Schneider Electric University has been designed to help data center professionals expand their skills by offering free guidance on the latest technology, sustainability, and energy efficiency initiatives.
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The ethics of artificial intelligence (AI) has become an important topic in the application of AI and machine learning in the past several years. This first part of a two-part series explains the evolution and importance of the ethics of AI. The second part will present its relevance and use in engineering applications.
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The 2021 Geothermal Experience Datathon focused on the application of analytics and data-science tools on oil and gas well-log data to assess geothermal potential in two North American basins.
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Earlier this year, 19 teams competed in a machine-learning contest held by the Data Analytics Study Group of SPE’s Gulf Coast Section. The was the first competition of its kind for SPE. Here, the organizers of the contest present some of the techniques used and lessons learned from the Machine Learning Challenge 2021.
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Smaller and independent upstream companies often have limited resources for data management. Nonetheless, their data are valuable and must be managed for that value to be realized. Geologists may just be in the perfect position to do the job, if they can get the training.
