Data & Analytics
Agentic AI could help upstream oil and gas operations reduce emissions by enabling real-time methane detection, optimizing flaring and energy use, and improving carbon capture efficiency.
This article examines how domain experts can use no-code ML platforms to explore decision-relevant problems, validate hypotheses, quickly build prototypes, and engage more effectively with data science teams when solutions transition toward production.
AI-driven analytics and digital platforms are reshaping offshore operations, enabling smarter, faster decision-making.
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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. -
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.
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Register today for the SPE AI Hackathon taking place 7–9 May in Dubai.
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The Unmanned Technical Section has updated its name to the Robotics and Autonomous Systems Technical Section.
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The oil and gas industry’s sustainability and success depend on its ability to cultivate and nurture a skilled and knowledgeable workforce.
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This article provides a mirror between the past and the present of well intervention technologies, including R&D to advance to downhole robots and autonomous intervention methods.
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Registration is open for the SPE Europe Energy GeoHackathon, which will be held in October and November. It will be preceded by 4-week online bootcamp sessions on data science and geothermal energy, which will begin on 2 October.
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Explore the challenges associated with fiber-optics data analysis and how recent advances in technology can be leveraged to maximize the value of the data.
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Join Serkan Dursun, Saudi Aramco, and Salem Algharbi, SDAIA, as they explore the capabilities and limitations of large language models GPT-3 and GPT-4 and ChatGPT and discuss their potential applications in the oil and gas industry.
