Shale Analytics : Data-Driven Analytics in Unconventional Resources / / by Shahab D. Mohaghegh

• Record Nr.: UNINA9910163986903321
• Autore: Mohaghegh Shahab D.
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
• ISBN: 9783319487533
• Edizione: [1st ed. 2017.]
• Descrizione fisica: 1 online resource (XIV, 287 p. 243 illus., 235 illus. in color.)
• Disciplina: 662.6
• Soggetti: Fossil fuels; Data mining; Geology, Economic; Mines and mineral resources; Geotechnical engineering; Fossil Fuels (incl. Carbon Capture); Data Mining and Knowledge Discovery; Economic Geology; Mineral Resources; Geotechnical Engineering & Applied Earth Sciences
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Data-Driven Formation Evaluation – Generation of Synthetic Geo-mechanical Well Logs in Shale -- Data-Driven Reservoir Characteristics – Impact of rock and completion parameters in -- Data-Driven Completion Analysis – Analysis, Design and Optimization of Hydraulic Fracturing in Shale -- Data-Driven Reservoir Modeling – Full Field Reservoir Modeling of Marcellus Shale -- Data-Driven Reservoir Modeling – Full Field Reservoir Modeling of Niobrara Formation, DJ Basin -- Data-Driven Reservoir Modeling – AI-Based Proxy of Numerical Reservoir Simulation of Shale.
• Sommario/riassunto: This book describes the application of modern information technology to reservoir modeling and well management. Data Driven Analytics in Unconventional Resources looks specifically at reservoir modeling and ; production management of shale reservoirs, since conventional reservoir modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the absence of well-understood and well-defined physics of fluid flow in shale. Also discussed are important insights into completion practices of production from shale Abundant examples and computer code are given that illustrate the operation of Data-Driven Analytics. The flexibility and power of the technique is demonstrated in numerous real-world situations.