deep learning
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The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.
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This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
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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 article presents a deep-learning approach, the long short-term memory network, for adaptive hydrocarbon production forecasting that takes historical operational and production information as input sequences to predict oil production as a function of operational plans.
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The authors of this paper describe a project that demonstrated the feasibility of using deep-learning and machine-learning approaches to introduce camera-based solids monitoring to the drilling industry.
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Large geological models are needed for modeling the subsurface processes in geothermal, carbon-storage, and hydrocarbon reservoirs. The size of these models contributes to the computational cost of history matching, engineering optimization, and forecasting. To reduce this cost, low-dimensional representations need to be extracted. Deep-learning tools, such as autoenc…
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The authors discuss the development of a deep-learning model to identify errors in simulation-based performance prediction in unconventional reservoirs.
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The authors of this paper propose a novel approach to data-driven modeling for transient production of oil wells.
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This paper describes an artificial intelligence deep Q network for field-development plan optimization.
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Eni and IBM developed a cognitive engine exploiting a deep-learning approach to scan documents, searching for basin geology concepts and extracting information about petroleum system elements.
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