Data & Analytics
This article from Murphy Oil outlines a modernization effort that resulted in increased scenario simulation capacity by five to 10 times.
The emerging vision is an industry where engineers focus on judgment, risk assessments, and accountability.
AI and machine learning are transforming drilling execution, detecting downhole problems earlier, automating reporting, and closing the loop between the wellbore and the well plan through real-time geosteering.
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The SPE Europe Energy GeoHackathon aims at educating and disseminating knowledge to all the participants on how data science applications can support geothermal energy developments and drive the energy transition. Boot camps begin 21 October. -
Experts at SPE’s Annual Technical Conference and Exhibition say that despite AI’s great potential, it’s important to be realistic about AI’s capabilities and to remember that successful projects solve specific business problems.
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Collaboration and technology will help the industry meet its toughest challenges, experts said during the opening session at ATCE.
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Prajakta Kulkarni, SPE, has spearheaded the development of a global digital platform to optimize pricing, strategy, and sales in the industry. With a background in petroleum engineering, she identified a digital gap in the industry, leading her to create a platform that enhances data-driven decision-making, streamlines operations, and integrates AI technologies to imp…
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SPE is excited to livestream these thought-provoking and informative Tech Talks from the SPE Energy Stream studio at the SPE Annual Technology Conference and Exhibition, 23–25 September, in New Orleans.
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As AI continues to evolve, the need for energy-powered data centers is on the rise. Data center developers who can make this transition toward a more efficient and greener system will anchor themselves as key players in this growing industry.
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The industry is balancing brains and bots as it squeezes out barrels of oil production.
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Explore how data science has become essential across diverse sectors, how people can learn about data science, and how engineers can transition into this field.
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In the final part of this three-part series, we extend our learning of Part 2 to the multivariate model and train a single model to predict three outcomes: oil, gas, and water.
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In Part 2 of this three-part series, we dive into a practical example using the production data of Equinor’s Volve field data set.
