DSDE: In Theory
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The authors of this paper propose an automated approach to sand prediction and control monitoring that improved operational efficiency by reducing time spent on manual analysis and the decision-making process in a Myanmar field.
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This paper focuses on characterization of fracture hits in the Eagle Ford, methods to predict their effects on production, and mitigation techniques.
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This paper explores electrical submersible generator design considerations, theoretical underpinnings, and potential future applications.
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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 authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.
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This paper presents a family of machine-learning-based reduced-order models trained on rigorous first-principle thermodynamic simulation results to extract physicochemical properties.
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In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.
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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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This paper describes a work flow that integrates data analysis, machine learning, and artificial intelligence to unlock the potential of large relative permeability databases.
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The objective of this paper is to present a fundamentals-based model of three-phase flow consistent with observation that avoids the pitfalls of conventional models such as Stone II or Baker’s three-phase permeability models.
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