2013
DOI: 10.1016/j.cageo.2013.04.001
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SeTES: A self-teaching expert system for the analysis, design, and prediction of gas production from unconventional gas resources

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Cited by 3 publications
(9 citation statements)
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“…The discussion and analysis in this section hews very closely to an earlier study (Moridis et al, 2013). Although the basic geological model and the production strategy remain the same as in the earlier study, new data, the consideration of heterogeneity, and the use of a 3D domain results in a drastically different reservoir simulation, with different grid geometry and different porous media properties.…”
Section: System Description and Production Strategymentioning
confidence: 77%
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“…The discussion and analysis in this section hews very closely to an earlier study (Moridis et al, 2013). Although the basic geological model and the production strategy remain the same as in the earlier study, new data, the consideration of heterogeneity, and the use of a 3D domain results in a drastically different reservoir simulation, with different grid geometry and different porous media properties.…”
Section: System Description and Production Strategymentioning
confidence: 77%
“…This study focuses on the oceanic hydrate deposits located in the Ulleung basin, continuing the previous studies of Moridis et al (2013).…”
Section: System Description and Geometrymentioning
confidence: 83%
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“…Their technique is an operational use of model-based optimization which requires a combination of long-term and shortterm objectives through multi-level optimization strategies. Moridis et al (2013) established a self-teaching expert system to increase oil production by improving flooding efficiency and reducing geological uncertainty.…”
Section: Closed-loop Reservoir Managementmentioning
confidence: 99%