Comparison between Genetic Algorithms and Differential Evolution for Solving the History Matching Problem

  • Elisa P. dos Santos Amorim
  • Carolina R. Xavier
  • Ricardo Silva Campos
  • Rodrigo W. dos Santos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7333)

Abstract

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 analyzed each of them differing on the number of parameters to be estimated: 2, 4, 9 and 16. Several tests are performed and the preliminary results are presented and discussed.

Keywords

Genetic Algorithm Inverse Problem History Match Reservoir Simulation Mutant Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Elisa P. dos Santos Amorim
    • 1
  • Carolina R. Xavier
    • 2
    • 3
  • Ricardo Silva Campos
    • 4
  • Rodrigo W. dos Santos
    • 4
  1. 1.Dept. of Computer ScienceUniversity of CalgaryCanada
  2. 2.Departamento de Ciência da ComputaçãoUFSJBrazil
  3. 3.COPPEUFRJBrazil
  4. 4.Programa de Pós Graduação em Modelagem ComputacionalUFJFJuiz de ForaBrazil

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