DSDE: In Practice
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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. -
The paper describes a project automating the progressing-cavity-pump well-modeling process wherein models are built and sustained automatically in a well-management system for all active PCP wells.
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The authors describe an integrated multiscale data methodology involving machine-leaning tools applied to the Late Jurassic Upper Jubaila formation outcrop data.
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The authors describe a logging-while-drilling nuclear-magnetic-resonance method applied in Bohai Bay, China, as an alternative to radioactive source-porosity measurements.
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In this work, a methodology to detect interference from long-term pressure and flow-rate data is developed using multiresolution analysis in combination with machine-learning algorithms.
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This paper describes an integrated work flow developed for 3D seismic reservoir characterization of deep and thin layers without sufficient well data in a South China Sea formation.
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Two well-test logging operations have been carried out for the first time in a conventional carbonate reservoir in safe operating conditions and with repeatable results.
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The machine-learning techniques applied aim to deliver a prediction model based on both simulation and real-time field data. The model tracks and monitors system key performance indicators.
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The paper demonstrates the ability of deep-learning generative models to enable new shale-characterization methods.
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The paper presents a model for shale gas production in which CO2 is injected by huff ’n’ puff into a hydraulic fracture surrounded by a shale matrix.
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