Computational Geosciences

, Volume 18, Issue 5, pp 747–762 | Cite as

Optimization of well placement by combination of a modified particle swarm optimization algorithm and quality map method

  • Shuaiwei DingEmail author
  • Hanqiao Jiang
  • Junjian Li
  • Guoping Tang


Determining the optimum placement of new wells in an oil field is a crucial work for reservoir engineers. The optimization problem is complex due to the highly nonlinearly correlated and uncertain reservoir performances which are affected by engineering and geologic variables. In this paper, the combination of a modified particle swarm optimization algorithm and quality map method (QM + MPSO), modified particle swarm optimization algorithm (MPSO), standard particle swarm optimization algorithm (SPSO), and centered-progressive particle swarm optimization (CP-PSO) are applied for optimization of well placement. The SPSO, CP-PSO, and MPSO algorithms are first discussed, and then the modified quality map method is discussed, and finally the implementation of these four methods for well placement optimization is described. Four example cases which involve depletion drive model, water injection model, and a real field reservoir model, with the maximization of net present value (NPV) as the objective function are considered. The physical model used in the optimization analyses is a 3-dimensional implicit black-oil model. Multiple runs of all methods are performed, and the results are averaged in order to achieve meaningful comparisons. In the case of optimizing placement of a single producer well, it is shown that it is not necessary to use the quality map to initialize the position of well placement. In other cases considered, it is shown that the QM + MPSO method outperforms MPSO method, and MPSO method outperforms SPSO and CP-PSO method. Taken in total, the modification of SPSO method is effective and the applicability of QM + MPSO for this challenging problem is promising


Well placement optimization Standard particle swarm optimization Centered-progressive particle swarm optimization Modified particle swarm optimization Quality map Geological uncertainty Reservoir simulation 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shuaiwei Ding
    • 1
    Email author
  • Hanqiao Jiang
    • 1
  • Junjian Li
    • 1
  • Guoping Tang
    • 1
  1. 1.Key Laboratory of Petroleum Engineering of the Ministry of EducationChina University of PetroleumBeijingChina

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