machine learning
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Few oil and gas companies give data science projects the better part of a decade to prove out, but that’s just what this one did.
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Data and AI will change our industry for the better. At the root of this change will be the empowerment of engineers to make better decisions.
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Machine learning has been shown to have a promising role in oil and gas explorations in recent years. Among the applications, determining a proper location for injection and production wells along with their optimal operating conditions is a complex problem.
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This article explains what deep learning is and how it works and presents an example use case from the energy industry.
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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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This article presents the application of a reinforcement learning control framework based on the Deep Deterministic Policy Gradient. The crack propagation process is simulated in Abaqus, which is integrated with a reinforcement learning environment to control crack propagation in brittle material. The real-world deployment of the proposed control framework is also dis…
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Geochemical parameters such as total organic carbon (TOC) provides valuable information to understand rock organic richness and maturity and, therefore, optimize hydrocarbon exploration. This article presents a novel work flow to predict continuous high-resolution TOC profiles using machine learning.
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The startup known for helping introduce Bitcoin mining to the upstream industry is now also offering cloud computing using associated gas.
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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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Texas A&M University-Qatar in collaboration with the SPE Qatar Section conducted a 2-day virtual workshop on flow assurance, carbon reduction, and digitalization. Participants included more than 200 professionals associated with academia, research institutes, and industry from 23 countries.