Computational Geosciences

, Volume 16, Issue 4, pp 1061–1079

Joint optimization of oil well placement and controls

  • Mathias C. Bellout
  • David Echeverría Ciaurri
  • Louis J. Durlofsky
  • Bjarne Foss
  • Jon Kleppe
Original Paper


Well placement and control optimization in oil field development are commonly performed in a sequential manner. In this work, we propose a joint approach that embeds well control optimization within the search for optimum well placement configurations. We solve for well placement using derivative-free methods based on pattern search. Control optimization is solved by sequential quadratic programming using gradients efficiently computed through adjoints. Joint optimization yields a significant increase, of up to 20% in net present value, when compared to reasonable sequential approaches. The joint approach does, however, require about an order of magnitude increase in the number of objective function evaluations compared to sequential procedures. This increase is somewhat mitigated by the parallel implementation of some of the pattern-search algorithms used in this work. Two pattern-search algorithms using eight and 20 computing cores yield speedup factors of 4.1 and 6.4, respectively. A third pattern-search procedure based on a serial evaluation of the objective function is less efficient in terms of clock time, but the optimized cost function value obtained with this scheme is marginally better.


Oil production optimization Optimal oil well placement Simulation-based optimization Derivative-free optimization Adjoint methods Mixed-integer nonlinear programming 


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Mathias C. Bellout
    • 1
  • David Echeverría Ciaurri
    • 2
  • Louis J. Durlofsky
    • 3
  • Bjarne Foss
    • 4
  • Jon Kleppe
    • 1
  1. 1.Department of Petroleum Engineering and Applied Geophysics, Center for Integrated Operations in the Petroleum IndustryNTNUTrondheimNorway
  2. 2.Department of Petroleum and Energy Analytics, T.J. Watson Research CenterIBMYorktown HeightsUSA
  3. 3.Department of Energy Resources EngineeringStanford UniversityStanfordUSA
  4. 4.Department of Engineering Cybernetics, Center for Integrated Operations in the Petroleum IndustryNTNUTrondheimNorway

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