Comparison of optimization techniques for automatic history matching

Reservoir parameters are estimated by adjusting simulation models to match field or laboratory data. Multivariate optimization techniques with physically realistic constraints on the parameters are used in order to obtain these estimates. Two examples are presented. The first example is the analysis...

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Autores principales: Savioli, G.B., Susana Bidner, M.
Formato: JOUR
Materias:
Permeability
Porosity
Reservoirs-Oil
Algorithms
Approximation theory
Capillary flow
Computational methods
Constraint theory
Mathematical models
Mechanical permeability
Optimization
Parameter estimation
Automatic algorithms
Automatic history matching
Coreflood experiment
Davidon Fletcher Powell (DFP)
Drawdown test
Fletcher Reeves (FR)
Levenberg Marquardt (LM)
Minimization
Multivariate optimization
Quasi Newton approximation for the least squares problem (QNA)
Petroleum reservoir evaluation
drawdown test
history matching
multivariate statistics
optimization
reservoir parameters
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_09204105_v12_n1_p25_Savioli
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
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spelling todo:paper_09204105_v12_n1_p25_Savioli2023-10-03T15:44:50Z Comparison of optimization techniques for automatic history matching Savioli, G.B. Susana Bidner, M. Permeability Porosity Reservoirs-Oil Algorithms Approximation theory Capillary flow Computational methods Constraint theory Mathematical models Mechanical permeability Optimization Parameter estimation Porosity Automatic algorithms Automatic history matching Coreflood experiment Davidon Fletcher Powell (DFP) Drawdown test Fletcher Reeves (FR) Levenberg Marquardt (LM) Minimization Multivariate optimization Quasi Newton approximation for the least squares problem (QNA) Petroleum reservoir evaluation drawdown test history matching multivariate statistics optimization reservoir parameters Reservoir parameters are estimated by adjusting simulation models to match field or laboratory data. Multivariate optimization techniques with physically realistic constraints on the parameters are used in order to obtain these estimates. Two examples are presented. The first example is the analysis of a drawndown test. Permeability and porosity are determined by minimizing an objective function which is the sum of the squares of the differences between theoretical and measured pressure-time distributions at the well. The minimization is performed by applying four different optimization techniques: Davidon-Fletcher-Powell (DFP), Fletcher-Reeves (FR), Quasi-Newton Approximation for the Least-Squares Problem (QNA) and Levenberg-Marquardt (LM). The second example is the simultaneous determination of capillary pressure and relative permeability curves of oil/water systems. It is based on the analysis of transient output data measured from a linear coreflood experiment. QNA and LM are used to match results from a numerical simulator to laboratory coreflood data. The special methods for the least-squares problem (LM, QNA) behave better than the two others (DFP, FR). LM and QNA arrive to the optimal point more frequently than DFP and FR. LM takes less computing time than QNA but is more affected by rounding errors. Therefore, QNA shows the best behavior when finding the optimum. The automatic algorithms are of particular use whenever the equations which govern the flow are too complex to be solved by the traditional analytical-graphical methods. © 1994. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_09204105_v12_n1_p25_Savioli
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Permeability
Porosity
Reservoirs-Oil
Algorithms
Approximation theory
Capillary flow
Computational methods
Constraint theory
Mathematical models
Mechanical permeability
Optimization
Parameter estimation
Porosity
Automatic algorithms
Automatic history matching
Coreflood experiment
Davidon Fletcher Powell (DFP)
Drawdown test
Fletcher Reeves (FR)
Levenberg Marquardt (LM)
Minimization
Multivariate optimization
Quasi Newton approximation for the least squares problem (QNA)
Petroleum reservoir evaluation
drawdown test
history matching
multivariate statistics
optimization
reservoir parameters
spellingShingle Permeability
Porosity
Reservoirs-Oil
Algorithms
Approximation theory
Capillary flow
Computational methods
Constraint theory
Mathematical models
Mechanical permeability
Optimization
Parameter estimation
Porosity
Automatic algorithms
Automatic history matching
Coreflood experiment
Davidon Fletcher Powell (DFP)
Drawdown test
Fletcher Reeves (FR)
Levenberg Marquardt (LM)
Minimization
Multivariate optimization
Quasi Newton approximation for the least squares problem (QNA)
Petroleum reservoir evaluation
drawdown test
history matching
multivariate statistics
optimization
reservoir parameters
Savioli, G.B.
Susana Bidner, M.
Comparison of optimization techniques for automatic history matching
topic_facet Permeability
Porosity
Reservoirs-Oil
Algorithms
Approximation theory
Capillary flow
Computational methods
Constraint theory
Mathematical models
Mechanical permeability
Optimization
Parameter estimation
Porosity
Automatic algorithms
Automatic history matching
Coreflood experiment
Davidon Fletcher Powell (DFP)
Drawdown test
Fletcher Reeves (FR)
Levenberg Marquardt (LM)
Minimization
Multivariate optimization
Quasi Newton approximation for the least squares problem (QNA)
Petroleum reservoir evaluation
drawdown test
history matching
multivariate statistics
optimization
reservoir parameters
description Reservoir parameters are estimated by adjusting simulation models to match field or laboratory data. Multivariate optimization techniques with physically realistic constraints on the parameters are used in order to obtain these estimates. Two examples are presented. The first example is the analysis of a drawndown test. Permeability and porosity are determined by minimizing an objective function which is the sum of the squares of the differences between theoretical and measured pressure-time distributions at the well. The minimization is performed by applying four different optimization techniques: Davidon-Fletcher-Powell (DFP), Fletcher-Reeves (FR), Quasi-Newton Approximation for the Least-Squares Problem (QNA) and Levenberg-Marquardt (LM). The second example is the simultaneous determination of capillary pressure and relative permeability curves of oil/water systems. It is based on the analysis of transient output data measured from a linear coreflood experiment. QNA and LM are used to match results from a numerical simulator to laboratory coreflood data. The special methods for the least-squares problem (LM, QNA) behave better than the two others (DFP, FR). LM and QNA arrive to the optimal point more frequently than DFP and FR. LM takes less computing time than QNA but is more affected by rounding errors. Therefore, QNA shows the best behavior when finding the optimum. The automatic algorithms are of particular use whenever the equations which govern the flow are too complex to be solved by the traditional analytical-graphical methods. © 1994.
format JOUR
author Savioli, G.B.
Susana Bidner, M.
author_facet Savioli, G.B.
Susana Bidner, M.
author_sort Savioli, G.B.
title Comparison of optimization techniques for automatic history matching
title_short Comparison of optimization techniques for automatic history matching
title_full Comparison of optimization techniques for automatic history matching
title_fullStr Comparison of optimization techniques for automatic history matching
title_full_unstemmed Comparison of optimization techniques for automatic history matching
title_sort comparison of optimization techniques for automatic history matching
url http://hdl.handle.net/20.500.12110/paper_09204105_v12_n1_p25_Savioli
work_keys_str_mv AT savioligb comparisonofoptimizationtechniquesforautomatichistorymatching
AT susanabidnerm comparisonofoptimizationtechniquesforautomatichistorymatching
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