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Inhaltsangabe: A major task of every oil company is oil field optimization, i.e. maximizing oil production and reducing operational cost. Knowledge about injector-producer relationships (IPRs) is crucial for optimal operation of oil fields. However, inferring IPRs has been a challenging problem due to the unknown underlying structure of oil fields, continuous change of the underlying structure over time, and the large number of wells, i.e. typically, hundreds of injection wells and hundreds of production wells. This book provides two different approaches which map the IPRs problem to a large-scale parameter estimation problem. One approach is constrained nonlinear optimization and the other is machine learning approach. The two approaches demonstrate that not only prediction accuracy but also computational efficiency can be achieved for large-scale parameter estimation problems. This book should help field engineers optimally operate oil fields and show researchers practical examples about how to apply optimization and machine learning techniques to oil field optimization.

Über den Autor: Hyokyeong Lee received her Ph.D. in computer science from University of Southern California in 2010. Her research interests are machine learning, optimization, and their applications to various fields including petroleum engineering. Before her Ph.D. study, she was a software engineer in the wireless communications division in Samsung Electronics.

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Buchbeschreibung Buchzustand: New. Publisher/Verlag: Scholar's Press | Optimization and Machine Learning Approaches | A major task of every oil company is oil field optimization, i.e. maximizing oil production and reducing operational cost. Knowledge about injector-producer relationships (IPRs) is crucial for optimal operation of oil fields. However, inferring IPRs has been a challenging problem due to the unknown underlying structure of oil fields, continuous change of the underlying structure over time, and the large number of wells, i.e. typically, hundreds of injection wells and hundreds of production wells. This book provides two different approaches which map the IPRs problem to a large-scale parameter estimation problem. One approach is constrained nonlinear optimization and the other is machine learning approach. The two approaches demonstrate that not only prediction accuracy but also computational efficiency can be achieved for large-scale parameter estimation problems. This book should help field engineers optimally operate oil fields and show researchers practical examples about how to apply optimization and machine learning techniques to oil field optimization. | Format: Paperback | Language/Sprache: english | 120 pp. Buchnummer des Verkäufers K9783639708622

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Buchbeschreibung Scholars' Press, 2014. PAP. Buchzustand: New. New Book. Delivered from our UK warehouse in 3 to 5 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Buchnummer des Verkäufers LQ-9783639708622

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Buchbeschreibung SPS Feb 2014, 2014. Taschenbuch. Buchzustand: Neu. 220x150x7 mm. Neuware - A major task of every oil company is oil field optimization, i.e. maximizing oil production and reducing operational cost. Knowledge about injector-producer relationships (IPRs) is crucial for optimal operation of oil fields. However, inferring IPRs has been a challenging problem due to the unknown underlying structure of oil fields, continuous change of the underlying structure over time, and the large number of wells, i.e. typically, hundreds of injection wells and hundreds of production wells. This book provides two different approaches which map the IPRs problem to a large-scale parameter estimation problem. One approach is constrained nonlinear optimization and the other is machine learning approach. The two approaches demonstrate that not only prediction accuracy but also computational efficiency can be achieved for large-scale parameter estimation problems. This book should help field engineers optimally operate oil fields and show researchers practical examples about how to apply optimization and machine learning techniques to oil field optimization. 120 pp. Englisch. Buchnummer des Verkäufers 9783639708622

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Buchbeschreibung SPS Feb 2014, 2014. Taschenbuch. Buchzustand: Neu. 220x150x7 mm. Neuware - A major task of every oil company is oil field optimization, i.e. maximizing oil production and reducing operational cost. Knowledge about injector-producer relationships (IPRs) is crucial for optimal operation of oil fields. However, inferring IPRs has been a challenging problem due to the unknown underlying structure of oil fields, continuous change of the underlying structure over time, and the large number of wells, i.e. typically, hundreds of injection wells and hundreds of production wells. This book provides two different approaches which map the IPRs problem to a large-scale parameter estimation problem. One approach is constrained nonlinear optimization and the other is machine learning approach. The two approaches demonstrate that not only prediction accuracy but also computational efficiency can be achieved for large-scale parameter estimation problems. This book should help field engineers optimally operate oil fields and show researchers practical examples about how to apply optimization and machine learning techniques to oil field optimization. 120 pp. Englisch. Buchnummer des Verkäufers 9783639708622

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Buchbeschreibung SPS Feb 2014, 2014. Taschenbuch. Buchzustand: Neu. 220x150x7 mm. This item is printed on demand - Print on Demand Neuware - A major task of every oil company is oil field optimization, i.e. maximizing oil production and reducing operational cost. Knowledge about injector-producer relationships (IPRs) is crucial for optimal operation of oil fields. However, inferring IPRs has been a challenging problem due to the unknown underlying structure of oil fields, continuous change of the underlying structure over time, and the large number of wells, i.e. typically, hundreds of injection wells and hundreds of production wells. This book provides two different approaches which map the IPRs problem to a large-scale parameter estimation problem. One approach is constrained nonlinear optimization and the other is machine learning approach. The two approaches demonstrate that not only prediction accuracy but also computational efficiency can be achieved for large-scale parameter estimation problems. This book should help field engineers optimally operate oil fields and show researchers practical examples about how to apply optimization and machine learning techniques to oil field optimization. 120 pp. Englisch. Buchnummer des Verkäufers 9783639708622

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Buchbeschreibung Scholars' Press, 2016. Paperback. Buchzustand: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Buchnummer des Verkäufers ria9783639708622_lsuk

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Buchbeschreibung Scholars' Press, 2014. Paperback. Buchzustand: New. book. Buchnummer des Verkäufers 3639708628

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Buchbeschreibung Scholars' Press. Paperback. Buchzustand: New. Paperback. 120 pages. Dimensions: 8.7in. x 5.9in. x 0.3in.A major task of every oil company is oil field optimization, i. e. maximizing oil production and reducing operational cost. Knowledge about injector-producer relationships (IPRs) is crucial for optimal operation of oil fields. However, inferring IPRs has been a challenging problem due to the unknown underlying structure of oil fields, continuous change of the underlying structure over time, and the large number of wells, i. e. typically, hundreds of injection wells and hundreds of production wells. This book provides two different approaches which map the IPRs problem to a large-scale parameter estimation problem. One approach is constrained nonlinear optimization and the other is machine learning approach. The two approaches demonstrate that not only prediction accuracy but also computational efficiency can be achieved for large-scale parameter estimation problems. This book should help field engineers optimally operate oil fields and show researchers practical examples about how to apply optimization and machine learning techniques to oil field optimization. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Buchnummer des Verkäufers 9783639708622

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