Data & Analytics
Venezuela’s oil recovery will depend on restoring disciplined, reliable day-to-day operations by stabilizing existing assets, fixing operational failures, and using practical tools to rebuild predictable production.
The free, virtual program is designed to help participants build data-driven capabilities for the energy industry.
Digital transformation in oil and gas depends less on adopting advanced technologies and more on maturing data so people and processes can reliably convert raw information into aligned, asset-level value.
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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.
