Application of Function Approximations to Reservoir Engineering: Estimation of Bubble Point Pressure, Oil FVF and GOR - Softcover
Synopsis
The book presents new approaches to address three of some of the most important ongoing challenges in petroleum engineering. Multiple Regression Analysis and two deferent artificial intelligence techniques (Neural Networks, ANNs and Least Squares Support Vector Machines, LS-SVM) are applied to: (1) Estimate bubble point pressure, bubble point oil FVF, bubble point GOR and stock-tank vent GOR in the absence of experimental analysis. Unlike the present PVT correlations, they can be applied in a straightforward manner by using direct field data. (2) Predict and interpolate average reservoir pressure. Three different models are obtained to predict and interpolate average reservoir pressure without closing the producing wells. (3) Forecast the production of oil reservoirs. ANNs and LS-SVM are applied to predict the performance of oil production within water injection reservoirs. The historical production and injection data are used as inputs. The approach can be categorized as a new and rapid method with reasonable results. Another application of these models is that it can be utilized to find the most economical scenario of water injection to maximize ultimate oil recovery.
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About the Author
Ezeddin Shirif is Professor of Petroleum Engineering at the University of Regina. He was an assistant professor of petroleum engineering at the University of Tripoli from 1982-1992. Dr. Shirif holds a BSc. Degree and MSc. degree from the University of Southern California and a PhD degree in EOR/reservoir simulation from the University of Alberta.
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Application of Function Approximations to Reservoir Engineering
Published by
LAP Lambert Academic Publishing Jan 2013, 2013
ISBN 10: 3659280291
ISBN 13: 9783659280290
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book presents new approaches to address three of some of the most important ongoing challenges in petroleum engineering. Multiple Regression Analysis and two deferent artificial intelligence techniques (Neural Networks, ANNs and Least Squares Support Vector Machines, LS-SVM) are applied to: (1) Estimate bubble point pressure, bubble point oil FVF, bubble point GOR and stock-tank vent GOR in the absence of experimental analysis. Unlike the present PVT correlations, they can be applied in a straightforward manner by using direct field data. (2) Predict and interpolate average reservoir pressure. Three different models are obtained to predict and interpolate average reservoir pressure without closing the producing wells. (3) Forecast the production of oil reservoirs. ANNs and LS-SVM are applied to predict the performance of oil production within water injection reservoirs. The historical production and injection data are used as inputs. The approach can be categorized as a new and rapid method with reasonable results. Another application of these models is that it can be utilized to find the most economical scenario of water injection to maximize ultimate oil recovery. 432 pp. Englisch. Seller Inventory # 9783659280290
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Application of Function Approximations to Reservoir Engineering
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ISBN 10: 3659280291
ISBN 13: 9783659280290
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shirif EzeddinEzeddin Shirif is Professor of Petroleum Engineering at the University of Regina. He was an assistant professor of petroleum engineering at the University of Tripoli from 1982-1992. Dr. Shirif holds a BSc. Degree and MS. Seller Inventory # 5145338
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Application of Function Approximations to Reservoir Engineering
Published by
LAP Lambert Academic Publishing Jan 2013, 2013
ISBN 10: 3659280291
ISBN 13: 9783659280290
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Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book presents new approaches to address three of some of the most important ongoing challenges in petroleum engineering. Multiple Regression Analysis and two deferent artificial intelligence techniques (Neural Networks, ANNs and Least Squares Support Vector Machines, LS-SVM) are applied to: (1) Estimate bubble point pressure, bubble point oil FVF, bubble point GOR and stock-tank vent GOR in the absence of experimental analysis. Unlike the present PVT correlations, they can be applied in a straightforward manner by using direct field data. (2) Predict and interpolate average reservoir pressure. Three different models are obtained to predict and interpolate average reservoir pressure without closing the producing wells. (3) Forecast the production of oil reservoirs. ANNs and LS-SVM are applied to predict the performance of oil production within water injection reservoirs. The historical production and injection data are used as inputs. The approach can be categorized as a new and rapid method with reasonable results. Another application of these models is that it can be utilized to find the most economical scenario of water injection to maximize ultimate oil recovery.Books on Demand GmbH, Überseering 33, 22297 Hamburg 432 pp. Englisch. Seller Inventory # 9783659280290
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Application of Function Approximations to Reservoir Engineering : Estimation of Bubble Point Pressure, Oil FVF and GOR
Published by
LAP Lambert Academic Publishing, 2013
ISBN 10: 3659280291
ISBN 13: 9783659280290
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book presents new approaches to address three of some of the most important ongoing challenges in petroleum engineering. Multiple Regression Analysis and two deferent artificial intelligence techniques (Neural Networks, ANNs and Least Squares Support Vector Machines, LS-SVM) are applied to: (1) Estimate bubble point pressure, bubble point oil FVF, bubble point GOR and stock-tank vent GOR in the absence of experimental analysis. Unlike the present PVT correlations, they can be applied in a straightforward manner by using direct field data. (2) Predict and interpolate average reservoir pressure. Three different models are obtained to predict and interpolate average reservoir pressure without closing the producing wells. (3) Forecast the production of oil reservoirs. ANNs and LS-SVM are applied to predict the performance of oil production within water injection reservoirs. The historical production and injection data are used as inputs. The approach can be categorized as a new and rapid method with reasonable results. Another application of these models is that it can be utilized to find the most economical scenario of water injection to maximize ultimate oil recovery. Seller Inventory # 9783659280290
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Application of Function Approximations to Reservoir Engineering: Estimation of Bubble Point Pressure, Oil FVF and GOR
Published by
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659280291
ISBN 13: 9783659280290
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