Data & Analytics
The energy sector is rapidly transforming toward a data-driven, decentralized future where combining human expertise with AI and machine learning unlocks new efficiencies, solves complex challenges, and creates a decisive competitive advantage.
Jim Clark, a reservoir engineer with more than 4 decades of experience, reflects on the evolution of subsurface engineering and CCS, emphasizing the growing importance of analytics, cross-disciplinary skills, and technical curiosity for the next generation of engineers.
Marie-Hélène Pelletier presents a proactive framework for building resilience, managing uncertainty, and maintaining performance as AI reshapes work and life.
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Continue our journey with Part 3 of this four-part series focused on addressing the implementation of AI in the petroleum industry using a real case study. -
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.
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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.
