Comparing Genetic Algorithms and Newton-Like Methods for the Solution of the History Matching Problem

Published on May 20, 2009 in ICCS (International Conference on Computational Science)
路 DOI :10.1007/978-3-642-01970-8_37
Elisa Portes dos Santos2
Estimated H-index: 2
,
Carolina Ribeiro Xavier8
Estimated H-index: 8
+ 2 AuthorsRodrigo Weber dos Santos21
Estimated H-index: 21
Sources
Abstract
In this work we presents a comparison of different optimization methods for the automatic history matching problem of reservoir simulation. The history matching process is an inverse problem that searches a set of parameters that minimizes the difference between the model performance and the historical performance of the field. This model validation process is essential and gives credibility to the predictions of the reservoir model. Derivative-based methods are compared to a free-derivative algorithm. In particular, we compare the Quasi-Newton method, non-linear Conjugate-Gradient, Steepest-Descent and a Genetic Algorithm implementation. Several tests are performed and the preliminary results are presented and discussed.
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2012ICCSA: International Conference on Computational Science and Its Applications
References10
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This book is a guide to the use of inverse theory for estimation and conditional simulation of flow and transport parameters in porous media. It describes the theory and practice of estimating properties of underground petroleum reservoirs from measurements of flow in wells, and it explains how to characterize the uncertainty in such estimates. Early chapters present the reader with the necessary background in inverse theory, probability and spatial statistics. The book demonstrates how to calcu...
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#1Zhangxin ChenH-Index: 56
List of figures List of tables List of notation Preface 1. Introduction 2. A glossary of petroleum terms 3. Single-phase flow and numerical solution 4. Well modeling 5. Two-phase flow and numerical solution 6. The black oil model and numerical solution 7. Transport of multicomponents in a fluid and numerical solution 8. Compositional flow and numerical solution 9. Nonisothermal flow and numerical solution 10. Practical topics in reservoir simulation Bibliography Index.
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A genetic algorithm is applied to the problem of conditioning the petrophysical rock properties of a reservoir model on historic production data. This is a difficult optimization problem where each evaluation of the objective function implies a flow simulation of the whole reservoir. Due to the high computing cost of this function, it is imperative to make use of an efficient optimization method to find a near optimal solution using as few iterations as possible. We have applied a genetic algori...
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Abstract In this work we present a study of genetic algorithms for the automatic history matching problem of reservoir simulation. The history matching process is an inverse problem that searches a set of parameters that minimizes the difference between the model performance and the historical performance of the field. This model validation process is essential and gives credibility to the predictions of the reservoir model. We studied a Parallel Genetic Algorithm implementation, several tests w...
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Jun 18, 2012 in ICCSA (International Conference on Computational Science and Its Applications)
#1Elisa Portes dos Santos Amorim (U of C: University of Calgary)H-Index: 5
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This work presents a performance comparison between Differential Evolution (DE) and Genetic Algorithms (GA), for the automatic history matching problem of reservoir simulations. The history matching process is an inverse problem that searches a set of parameters that minimizes the difference between the model performance and the historical performance of the field. This model validation process is essential and gives credibility to the predictions of the reservoir model. Four case studies were a...
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