Data & Analytics
In an industry that rarely slows down, memory can be a powerful engineering tool. Not in terms of nostalgia, but in perspective. Many of the activities that constituted daily operations have been so deeply transformed that new generations of engineers may never have experienced them before.
Machine learning is transforming equipment reliability by enabling predictive maintenance, improving safety, and reducing costly downtime across drilling, production, pipelines, and CCUS operations.
This article explores how AI is transforming oil and gas operations, including its real impact on methane reduction, predictive maintenance, energy efficiency, and whether it truly delivers measurable sustainability gains or just adds complexity.
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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. -
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.
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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.
