1887

Abstract

Summary

We propose an efficient approach to well placement optimization with meta-optimized hybrid global and local algorithm. First, we take advantage of Cat Swarm Optimization (CSO) algorithm, which has good exploration ability in the global search space, and Mesh Adaptive Directive Search (MADS) algorithm, which can provide efficient local exploration, to establish a hybrid algorithm. Then we use meta-optimization method to determine the proper controlling parameters for this new hybrid algorithm. Our proposed optimization approach is applied in a semi-synthetic model. Besides, we compare the optimization performance of the proposed optimization approach with that of CSO, MADS, hybrid optimization algorithm with specific controlling parameters. Results indicate that hybrid algorithm shows better exploration ability in both global and local space than stand-alone CSO and MADS. Our proposed algorithm outperforms hybrid algorithm. It demonstrates that controlling parameters have significant impact on optimization efficiency of hybrid algorithm. With the meta-optimization method, optimization performance of hybrid optimization algorithm is improved. The proposed approach can serve as an efficient tool to determine optimal well placement to improve oil recovery under sustained low oil price. Besides, it could be useful in other petroleum engineering problems such as well control optimization and fracture parameters optimization.

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/content/papers/10.3997/2214-4609.201801223
2018-06-11
2020-07-04
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References

  1. Atashnezhad, A., Wood, D., Fereidounpour, A. and Khosravanian, R.
    [2014] Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms. Journal of Natural Gas Science and Engineering, 21, 1184–1204.
    [Google Scholar]
  2. Chen, H., Feng, Q., Zhang, X., Wang, S., Zhou, W. and Geng, Y.
    [2017] Well placement optimization using an analytical formula-based objective function and cat swarm optimization algorithm. Journal of Petroleum Science and Engineering, 157, 1067–1083.
    [Google Scholar]
  3. Christie, M. and Blunt, M.
    [2001] Tenth SPE comparative solution project: a comparison of upscaling techniques. SPE Reservoir Evaluation & Engineering, 4(4), 308–317.
    [Google Scholar]
  4. Humphries, T. and Haynes, R.
    [2015] Joint optimization of well placement and control for nonconventional well types. Journal of Petroleum Science and Engineering, 126, 242–253.
    [Google Scholar]
  5. Isebor, O., Durlofsky, L. and Ciaurri, D.
    [2014] A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls. Computational Geosciences, 18(3–4), 463–482.
    [Google Scholar]
  6. Meissner, M., Schmuker, M. and Schneider, G.
    [2006] Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics, 7(1), 125.
    [Google Scholar]
  7. Nwankwor, E., Nagar, A. and Reid, D.
    [2013] Hybrid differential evolution and particle swarm optimization for optimal well placement. Computational Geosciences, 17, 1–20.
    [Google Scholar]
  8. Pradhan, P., Panda, G.,
    2012. Solving multiobjective problems using cat swarm optimization. Expert Systems with Application, 39(3), 2956–2964.
    [Google Scholar]
  9. Wang, X., Haynes, R.D. and Feng, Q.
    [2016] A multilevel coordinate search algorithm for well placement, control and joint optimization. Computers & Chemical Engineering, 95, 75–96.
    [Google Scholar]
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