neural networks
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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. -
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 presents a physics-informed neural network technique able to use information from fluid-flow physics as well as observed data to model the Buckley-Leverett problem.
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The authors develop a methodology that calculates the mechanical specific energy using real-time drillstring acceleration signals directly.
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The authors of this paper present a method for prediction of sucker-rod-pump failure based on improved, completely connected perceptron artificial neural networks.
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This paper analyzes several configurations of convolutional neural networks suited for predicting upscaled fracture permeabilities and shape factors required to close a dual porosity/dual permeability model.
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The paper describes an end-to-end deep surrogate model capable of modeling field and individual-well production rates given arbitrary sequences of actions.
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The proposed solution is a good candidate for real-time burner-efficiency monitoring and automatic alarm triggering and optimization.
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The work and the provided methodology provide a significant improvement in facies classification.
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In the complete paper, the authors generate a model by using an artificial-neural-network (ANN) technique to predict both capillary pressure and relative permeability from resistivity.
