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Soft Computing on Reservoir Characterization & Production Forecasting: Application of Higher-order Neural Network on Production Forecasting and Adaptive Genetic Algorithm for History Matching - Softcover
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Production forecasting and reservoir modeling play vital roles in optimal field development plan and management of petroleum reservoirs. This motivates engineers to develop computationally efficient and fast numerical methods capable of constructing history matched reservoir models producing reliable production forecasts. Relatively two new soft computing techniques successfully applied for automatic history matching and production forecasting. The first approach is artificial neural networks (ANN) based modeling, and the 2nd is genetic algorithm (GA) based optimization. A higher-order neural network (HONN) with higher-order synaptic operation (HOSO) architecture that embeds linear (conventional), quadratic (QSO) and cubic synaptic operations (CSO) used for forecasting real field oil production. For automatic history matching problem through reservoir characterization, a global optimization method called adaptive genetic algorithm (AGA) was employed. Adaptive genetic operators of AGA dynamically adjusts control parameters during evolution. The performance of both soft computing methods in achieving fast convergence rate and reduced computational efforts are presented in this book.
Biografía del autor
Dr. Chithra Chakra holds Ph.D. in Computer Science & Engineering from University of Petroleum & Energy Studies, India, working as Research Engineer in ADRIC- The Petroleum Institute, Abu Dhabi. Her research focus on reservoir modeling and simulation, evolutionary algorithms, gradient and stochastic production optimization methods.
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- VerlagLAP LAMBERT Academic Publishing
- Erscheinungsdatum2017
- ISBN 10 365991777X
- ISBN 13 9783659917776
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten256
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Soft Computing on Reservoir Characterization & Production Forecasting
Verlag:
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 365991777X
ISBN 13: 9783659917776
Neu
Softcover
Anbieter: moluna, Greven, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chakra N C ChithraDr. Chithra Chakra holds Ph.D. in Computer Science & Engineering from University of Petroleum & Energy Studies, India, working as Research Engineer in ADRIC- The Petroleum Institute, Abu Dhabi. Her research focus on. Bestandsnummer des Verkäufers 151429772
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Soft Computing on Reservoir Characterization & Production Forecasting
Verlag:
LAP LAMBERT Academic Publishing Feb 2017, 2017
ISBN 10: 365991777X
ISBN 13: 9783659917776
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Production forecasting and reservoir modeling play vital roles in optimal field development plan and management of petroleum reservoirs. This motivates engineers to develop computationally efficient and fast numerical methods capable of constructing history matched reservoir models producing reliable production forecasts. Relatively two new soft computing techniques successfully applied for automatic history matching and production forecasting. The first approach is artificial neural networks (ANN) based modeling, and the 2nd is genetic algorithm (GA) based optimization. A higher-order neural network (HONN) with higher-order synaptic operation (HOSO) architecture that embeds linear (conventional), quadratic (QSO) and cubic synaptic operations (CSO) used for forecasting real field oil production. For automatic history matching problem through reservoir characterization, a global optimization method called adaptive genetic algorithm (AGA) was employed. Adaptive genetic operators of AGA dynamically adjusts control parameters during evolution. The performance of both soft computing methods in achieving fast convergence rate and reduced computational efforts are presented in this book. 256 pp. Englisch. Bestandsnummer des Verkäufers 9783659917776
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Soft Computing on Reservoir Characterization & Production Forecasting : Application of Higher-order Neural Network on Production Forecasting and Adaptive Genetic Algorithm for History Matching
Verlag:
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 365991777X
ISBN 13: 9783659917776
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Production forecasting and reservoir modeling play vital roles in optimal field development plan and management of petroleum reservoirs. This motivates engineers to develop computationally efficient and fast numerical methods capable of constructing history matched reservoir models producing reliable production forecasts. Relatively two new soft computing techniques successfully applied for automatic history matching and production forecasting. The first approach is artificial neural networks (ANN) based modeling, and the 2nd is genetic algorithm (GA) based optimization. A higher-order neural network (HONN) with higher-order synaptic operation (HOSO) architecture that embeds linear (conventional), quadratic (QSO) and cubic synaptic operations (CSO) used for forecasting real field oil production. For automatic history matching problem through reservoir characterization, a global optimization method called adaptive genetic algorithm (AGA) was employed. Adaptive genetic operators of AGA dynamically adjusts control parameters during evolution. The performance of both soft computing methods in achieving fast convergence rate and reduced computational efforts are presented in this book. Bestandsnummer des Verkäufers 9783659917776
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Soft Computing on Reservoir Characterization & Production Forecasting: Application of Higher-order Neural Network on Production Forecasting and Adaptive Genetic Algorithm for History Matching
Verlag:
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 365991777X
ISBN 13: 9783659917776
Neu
Paperback
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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Paperback. Zustand: Brand New. 256 pages. 8.66x5.91x0.58 inches. In Stock. Bestandsnummer des Verkäufers 365991777X
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