AI/machine learning
Case studies from Devon Energy, ConocoPhillips, and others show how clever engineering and technology are being used to improve recovery and asset value.
This case study from Tomax outlines the use of artificial intelligence to analyze high-fidelity data of the full drilling process.
This study demonstrates the transformative potential of physics-informed neural networks as a hybrid framework for multiphase flow measurement.
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SponsoredThis Q&A highlights the benefits of AI and ML to automate work flows and analyze data at a much faster rate—within minutes. These capabilities deliver a fit-for-basin approach designed specifically for US-centric work flows. -
Highlighting news on the recent SPE Board of Directors meeting in Saudi Arabia and SPE’s utilization of artificial intelligence now and its plans for the future.
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Researchers with the Energy & Environmental Research Center highlight the key use cases ChatGPT holds today for petrotechnicals.
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ChatGPT and its derivative artificial intelligence chatbots are fulfilling the needs and satisfying the curiosity of novices and more experienced AI users. Have you gotten your feet wet or dove right in?
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For organizations that do it well, data management provides a competitive edge in an increasingly digital oil field. But teams all too often are so busy managing all the moving parts of data management that they take their eye off of “the prize”—the payoff after you have put everything into place to sustain successful data management.
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This paper details experiences gained while developing a novel technology-driven approach to risk assessment methodologies such as process hazard analysis, hazard identification, and hazard operability in oil and gas.
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SPE Data Science and Engineering Analytics Technical Director Silviu Livescu and SPE Reservoir Technical Director Rodolfo Camacho address some of the challenges in the application of data analytics, artificial intelligence, and machine learning to several reservoir engineering problems.
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The use of artificial intelligence in the clean energy sector increases the availability and accessibility of clean energy, making it a more viable and cost-effective alternative to traditional energy sources.
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The authors of this paper discuss a global rate-of-penetration machine-learning model with the potential to eliminate learning curves and reduce time and costs associated with developing a new model for every field.
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The authors of this paper describe a project that demonstrated the feasibility of using deep-learning and machine-learning approaches to introduce camera-based solids monitoring to the drilling industry.
