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

Content Provider	Springer Nature Link
Author	Ding, Shuaiwei Jiang, Hanqiao Li, Junjian Tang, Guoping
Copyright Year	2014
Abstract	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
Starting Page	747
Ending Page	762
Page Count	16
File Format	PDF
ISSN	14200597
Journal	Computational Geosciences
Volume Number	18
Issue Number	5
e-ISSN	15731499
Language	English
Publisher	Springer International Publishing
Publisher Date	2014-05-29
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Well placement optimization Standard particle swarm optimization Centered-progressive particle swarm optimization Modified particle swarm optimization Quality map Geological uncertainty Reservoir simulation Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Mathematical Modeling and Industrial Mathematics Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Computers in Earth Sciences Computational Theory and Mathematics Computer Science Applications Computational Mathematics

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