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Computational Geosciences

, Volume 16, Issue 1, pp 75–92 | Cite as

Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models

  • Zyed Bouzarkouna
  • Didier Yu Ding
  • Anne Auger
Original Paper

Abstract

The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the non-regularity of the objective function. In this paper, we propose an optimization methodology for determining optimal well locations and trajectories based on the covariance matrix adaptation evolution strategy (CMA-ES) which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. In addition, to improve the optimization procedure, two new techniques are proposed: (a) adaptive penalization with rejection in order to handle well placement constraints and (b) incorporation of a meta-model, based on locally weighted regression, into CMA-ES, using an approximate stochastic ranking procedure, in order to reduce the number of reservoir simulations required to evaluate the objective function. The approach is applied to the PUNQ-S3 case and compared with a genetic algorithm (GA) incorporating the Genocop III technique for handling constraints. To allow a fair comparison, both algorithms are used without parameter tuning on the problem, and standard settings are used for the GA and default settings for CMA-ES. It is shown that our new approach outperforms the genetic algorithm: It leads in general to both a higher net present value and a significant reduction in the number of reservoir simulations needed to reach a good well configuration. Moreover, coupling CMA-ES with a meta-model leads to further improvement, which was around 20% for the synthetic case in this study.

Keywords

Well placement Field development optimization CMA-ES Evolution strategies Meta-models 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Zyed Bouzarkouna
    • 1
    • 2
  • Didier Yu Ding
    • 1
  • Anne Auger
    • 2
  1. 1.IFP Energies nouvellesRueil-Malmaison CedexFrance
  2. 2.TAO Team, INRIA Saclay-Île-de-France, LRIParis-Sud UniversityOrsay CedexFrance

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