Data & Analytics
Digital drilling technologies are enabling a shift toward more predictive, efficient, and sustainable operations.
Breakthroughs in energy, similar to those seen in AI, require coordinated progress across multiple fields and the resolution of structural bottlenecks. As a result, a successful energy transition depends on integrated advances in infrastructure, policy, technology, and investment rather than isolated efforts.
The Genesis Mission is a US Department of Energy initiative that integrates AI, national labs, and cross-sector collaboration to accelerate scientific discovery, strengthen energy innovation, and enhance national security.
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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. -
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.
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In Part 1 of this three-part series, we use long short-term memory (LSTM), a machine learning technique, to predict oil, gas, and water production using real field data.
