Computational Geosciences

, Volume 18, Issue 3–4, pp 463–482 | Cite as

A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls

  • Obiajulu J. IseborEmail author
  • Louis J. Durlofsky
  • David Echeverría Ciaurri


In oil field development, the optimal location for a new well depends on how it is to be operated. Thus, it is generally suboptimal to treat the well location and well control optimization problems separately. Rather, they should be considered simultaneously as a joint problem. In this work, we present noninvasive, derivative-free, easily parallelizable procedures to solve this joint optimization problem. Specifically, we consider Particle Swarm Optimization (PSO), a global stochastic search algorithm; Mesh Adaptive Direct Search (MADS), a local search procedure; and a hybrid PSO–MADS technique that combines the advantages of both methods. Nonlinear constraints are handled through use of filter-based treatments that seek to minimize both the objective function and constraint violation. We also introduce a formulation to determine the optimal number of wells, in addition to their locations and controls, by associating a binary variable (drill/do not drill) with each well. Example cases of varying complexity, which include bound constraints, nonlinear constraints, and the determination of the number of wells, are presented. The PSO–MADS hybrid procedure is shown to consistently outperform both stand-alone PSO and MADS when solving the joint problem. The joint approach is also observed to provide superior performance relative to a sequential procedure.


Derivative-free optimization Field development optimization Well placement Production optimization Reservoir simulation-based optimization Nonlinear programming 

Mathematics Subject Classifications (2010)

90-08 90C11 90C26 90C30 90C56 90C90 


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Obiajulu J. Isebor
    • 1
    Email author
  • Louis J. Durlofsky
    • 1
  • David Echeverría Ciaurri
    • 2
  1. 1.Department of Energy Resources EngineeringStanford UniversityStanfordUSA
  2. 2.T. J. Watson Research Center, IBMYorktown HeightsUSA

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