A Mixed-integer Nonlinear Optimization Approach for Well Placement and Geometry

References

1. AbdulKarim, A., Al-Dhubaib, T., Elrafie, E. and Alamoudi, M.
   [2010] Overview of saudi aramco’s intelligent field program. SPE Intelligent Energy Conference and Exhibition, Jaarbeurs, Utrecht, The Netherlands, March 23–25, SPE.
   [Google Scholar]
2. Audet, C. and Dennis, J.J.E.
   [2001] Pattern search algorithms for mixed variable programming. Siam J. Optim., 11(3), 573–594.
   [Google Scholar]
3. Bonami, P., Biegler, L.T., Conn, A.R., Cornuéjols, G., Grossmann, I.E., Laird, C.D., Lee, J., Lodi, A., Margot, F., Sawaya, N. and Wächter, A.
   [2008] An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optimization, 5(2), 186–204.
   [Google Scholar]
4. Bouzarkouna, Z., Ding, D.Y. and Auger, A.
   [2010] Using evolution strategy with meta-models for well placement optimization. CoRR, abs/1011.5481.
   [Google Scholar]
5. Chiles, J.P.
   [1984] Simulation of a nickel deposit: problems encountered and practical solutions.
   [Google Scholar]
6. Dakin, R.J.
   [1965] A tree search algorithm for mixed-integer programming problems. Computer Journal, (8).
   [Google Scholar]
7. Echeverría Ciaurri, D., Isebor, O. and Durlofsky, L.
   [2011] Application of derivative-free methodologies to generally constrained oil production optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 2(2), 134–161.
   [Google Scholar]
8. Fourer, R., Gay, D.M. and Kernighan, B.W.
   [2003] AMPL : a modeling language for mathematical programming / Robert Fourer, David M. Gay, Brian W. Kernighan. Pacific Grove, CA : Thomson/Brooks/Cole, 2nd edn., ISBN 0534388094.
   [Google Scholar]
9. Gramacy, R. and Le Digabel, S.
   [2011] The mesh adaptive direct search algorithm with treed gaussian process surrogates. Tech. rep., Les Cahiers du GERAD G-2011-37.
   [Google Scholar]
10. Gupta, O.K. and Ravindran, V.
    [1985] Branch and bound experiments in convex nonlinear programming. Management Science, 31(12), 1533–1546.
    [Google Scholar]
11. Isebor, O.J., Echeverría Ciaurri, D. and Durlofsky, L.J.
    [2013] Generalized field development optimization using derivative-free procedures. Society of Petroleum Engineers Journal.
    [Google Scholar]
12. Le Digabel, S.
    [2011] Algorithm 909: Nomad: Nonlinear optimization with the mads algorithm. ACM Trans. Math. Softw., 37(4), 44:1–44:15, ISSN 0098–3500, doi:10.1145/1916461.1916468.
    https://doi.org/10.1145/1916461.1916468 [Google Scholar]
13. Le Ravalec, M., Tillier, E., Da Veiga, S., Enchery, G. and Gervais, V.
    [2012] Advanced integrated workflows for incorporating both production and 4d seismic-related data into reservoir models. Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles, 67(2), 207–220, doi:10.2516/ogst/2011159.
    https://doi.org/10.2516/ogst/2011159 [Google Scholar]
14. Le Ravalec, M., Noetinger, B. and Hu, L.Y.
    [2000] The fft moving average (fft-ma) generator: An efficient numerical method for generating and conditioning gaussian simulations. Mathematical Geology, 32(6), 701–723.
    [Google Scholar]
15. Liberti, L., Mladenović, N. and Nannicini, G.
    [2011] A recipe for finding good solutions to minlps. Mathematical Programming Computation, 3(4), 349–390, ISSN 1867–2949, doi:10.1007/s12532‑011‑0031‑y.
    https://doi.org/10.1007/s12532-011-0031-y [Google Scholar]
16. Matheron, G., Beucher, H., de Fouquet, C. and Galli, A.
    [1987] Conditional simulation of the geometry of fluvio-deltaic reservoirs. Ann. Techn. Conf. and Exhib., Dallas, TX, SPE 16753.
    [Google Scholar]
17. Oliver, D., Reynolds, A. and Liu, N.
    [2008] Inverse theory for petroleum reservoir characterization and history-matching. Cambridge University Press, New York, USA.
    [Google Scholar]
18. Onwunalu, J.E. and Durlofsky, L.J.
    [2010] Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences, 14, 198–.
    [Google Scholar]
19. Peters, L., Arts, R., Brouwer, G., Geel, C., Cullick, S., Lorentzen, R., Chen, Y., Dunlop, N., Vossepoel, F., Xu, R., Sarma, P., Alhuthali, A. and Reynolds, A.
    [2010] Results of the brugge benchmark study for flooding optimization and history matching. SPE Reservoir Evaluation & Engineering, 13, 391–405, doi:10.2118/119094‑PA.
    https://doi.org/10.2118/119094-PA [Google Scholar]
20. PumaFlow
    [2012] PumaFlow Reservoir Simulator Reference Manual. BeicipFranlab.
    [Google Scholar]
21. Schulze-Riegert, R. and Ghedan, S.
    [2007] Modern techniques for history-matching. 9th International Forum on Reservoir Simulation, Abu Dhabi, UEA, December 9–13.
    [Google Scholar]
22. van den Berg, F., Perrons, R., Moore, I. and Schut, G.
    [2010] Business value from intelligent fields. SPE Intelligent Energy Conference and Exhibition, Jaarbeurs, Utrecht, The Netherlands, March 23–25, SPE.
    [Google Scholar]