A modified differential evolution optimization algorithm with random localization for generation of best-guess properties in history matching

Rahmati, H ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1080/15567030903261832
  3. Publisher: 2011
  4. Abstract:
  5. Computer aided history matching techniques are increasingly playing a role in reservoir characterization. This article describes the implementation of a differential evolution optimization algorithm to carry out reservoir characterization by conditioning the reservoir simulation model to production data (history matching). We enhanced the differential evolution algorithm and developed the modified differential evolution optimization method with random localization. The proposed technique is simple-structured, robust, and computationally efficient. We also investigated the convergence characteristics of the algorithm in some synthetic oil reservoirs. In addition, the proposed method is compared with the Nelder-Mead simplex search method and a standard Genetic algorithm
  6. Keywords:
  7. Computationally efficient ; Computer aided ; Convergence characteristics ; Differential Evolution ; Differential evolution algorithms ; Differential evolution optimization algorithms ; History matching ; Modified differential evolution ; Nelder-Mead simplex search method ; Production data ; Reservoir characterization ; Reservoir simulation ; Reservoir simulation model ; Standard genetic algorithm ; Synthetic oil ; Biology ; Computer simulation ; Convergence of numerical methods ; Evolutionary algorithms ; Optimization ; Petroleum reservoir engineering ; Petroleum reservoirs
  8. Source: Energy Sources, Part A: Recovery, Utilization and Environmental Effects ; Volume 33, Issue 9 , Feb , 2011 , Pages 845-858 ; 15567036 (ISSN)
  9. URL: http://www.tandfonline.com/doi/abs/10.1080/15567030903261832