data analytics
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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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The partnership plans to develop an overlying software suite, Data Mesh, to consolidate data from various sources and increase data-access efficiency.
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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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This paper summarizes techniques for production allocation using geochemical methods and describes a best practice for a specialized approach.
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Understanding the subsurface is crucial to the success of carbon capture and storage, and digital solutions are essential for an accurate analysis of the subsurface being considered.
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Health, safety, and environment operations can be greatly enhanced by using artificial intelligence (AI) techniques on HSE data. One important aspect inherent in this process is the need to establish trust in the AI system among the users.
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Decades of experience injecting fluids into the ground has revealed a fundamental truth: No two injection sites are the same. A thorough understanding of site-specific conditions is essential to ensure safe and secure long-term subsurface disposal of carbon dioxide.
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The company said its expanded XIO series of remote controllers provides real-time monitoring and control, improved data accessibility, and enhanced data integrity.
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The Angolan oil and gas producer will use the Palantir Foundry software suite across its operations.
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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…