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Abstract

Summary

A key component of the reservoir management is to take decisions about the life-cycle development or re-development activities in the oil and gas reservoirs. Implementing optimization procedure in real fields usually has encountered with operational challenges. These are the multi-objective conflicts, high number of parameter to be optimized, high computational cost, and different decision maker levels. This motivates us to design an integrated workflow for field development optimization met the mentioned concern. The methodology can be handle different technical and economical objectives simultaneously to help a better decision making process. In addition, a decision gate has been considered after each level of optimization; these gates are aligned with decision taking levels in the reservoir management procedure. Proposed workflow has been implemented in a giant offshore field; optimum scenario for IOR/EOR activates to sustain the production has been planned. Flexibility in multi-level optimization workflow causes that it can be customized for any type field regarding its condition.

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/content/papers/10.3997/2214-4609.201801200
2018-06-11
2024-03-28
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References

  1. Abd Karim, M.G., Abd Raub, M.R. Bin
    , 2011. Optimizing Development Strategy and Maximizing Field Economic Recovery through Simulation Opportunity Index, in: SPE Reservoir Characterisation and Simulation Conference and Exhibition. pp. 2–7. doi:10.2118/148103‑MS
    https://doi.org/10.2118/148103-MS [Google Scholar]
  2. Almeida, L.F., Tupac, Y.J., Lazo, J.G.L., Pacheco, M.A.C., Vellasco, M.M.B.R.
    , 2007. Evolutionary Optimization of Smart-Wells Control Under Technical Uncertainties, in: Latin American & Caribbean Petroleum Engineering Conference. SPE 107872, Buenos Aires, Argentina. doi:10.2118/107872‑ms
    https://doi.org/10.2118/107872-ms [Google Scholar]
  3. Awotunde, A.A., Sibaweihi, N.
    , 2014. Consideration of Voidage-Replacement Ratio in Well-Placement Optimization. SPE Econ. Manag. 40–54.
    [Google Scholar]
  4. Bagherinezhad, A., Bozorgmehry Boozarjomehry, R., Pishvaie, M.R.
    , 2017. Multi-Criterion Based Well Placement and Control in the Water-Flooding of Naturally Fractured Reservoir. J. Pet. Sci. Eng.149, 675–685. doi:10.1016/j.petrol.2016.11.013
    https://doi.org/10.1016/j.petrol.2016.11.013 [Google Scholar]
  5. Bellout, M.C., Ciaurri, D.E., Durlofsky, L.J., Foss, B., Kleppe, J.
    , 2011. Joint Optimization of Oil Well Placement and Controls. Int. J. Math. Model. Numer. Optim.16, 1061–1079.
    [Google Scholar]
  6. Doublet, D.C., Aanonsen, S.I., Tai, X.C.
    , 2009. An efficient method for smart well production optimisation. J. Pet. Sci. Eng.69, 25–39. doi:10.1016/j.petrol.2009.06.008
    https://doi.org/10.1016/j.petrol.2009.06.008 [Google Scholar]
  7. Echeverria Ciaurri, D., Isebor, O.J., Durlofsky, L.J.
    , 2011. Application of derivative-free methodologies to generally constrained oil production optimization problems. Int. J. Math. Model. Numer. Optimisation2, 134–161. doi:10.1016/j.procs.2010.04.145
    https://doi.org/10.1016/j.procs.2010.04.145 [Google Scholar]
  8. Gross, H.
    , 2012. Response surface approaches for large decision trees: decision making under uncertainty, in: ECMOR XIII-13th European Conference on the Mathematics of Oil Recovery.
    [Google Scholar]
  9. Humphries, T.D., Haynes, R.D., James, L. a.
    , 2014. Simultaneous and sequential approaches to joint optimization of well placement and control. Comput. Geosci.18, 433–448. doi: 10.1007/s10596‑013‑9375‑x
    https://doi.org/10.1007/s10596-013-9375-x [Google Scholar]
  10. Isebor, O.J., Echeverri, D., Durlofsky, L.J.
    , 2014. Generalized Field-Development Optimization With Derivative-Free Procedures. SPE J.891–908.
    [Google Scholar]
  11. Jansen, J., Bosgra, O.H., Van den Hof, P.M.J.
    , 2008. Model-based control of subsurface flow. J. Process Control1–17.
    [Google Scholar]
  12. Jansen, J.D., Douma, S.D., Brouwer, D.R., Van der Hof, P.M.J., Bosgra, O.H., Heemink, A.W.
    , 2009. Closed-Loop Reservoir Management, in: SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, Woodlands, Texas. doi:10.2118/119098‑MS
    https://doi.org/10.2118/119098-MS [Google Scholar]
  13. Lorentzen, R.J., Berg, A.M., Nævdal, G., Vefring, E.H.
    , 2006. A New Approach for Dynamic Optimization of Waterflooding Problems, in: SPE Intelligent Energy Conference and Exhibition. SPE 99690, Amsterdam, The Netherlands.
    [Google Scholar]
  14. Onwunalu, J.E., Durlofsky, L.J.
    , 2010. Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput. Geosci.14, 183–198. doi:10.1007/s10596‑009‑9142‑1
    https://doi.org/10.1007/s10596-009-9142-1 [Google Scholar]
  15. Shirangi, M.G., Durlofsky, L.J.
    , 2015. Closed-Loop Field Development Optimization Under Uncertainty, in: SPE Reservoir Simulation Symposium. SPE 173219-MS, Houston, Texas, pp. 1–22.
    [Google Scholar]
  16. Taware, S.V., Park, H., Datta-Gupta, A., Bhattacharya, S., Tomar, a K., Kumar, M., Rao, H.S.
    , 2012. Well placement optimization in a mature carbonate waterflood using streamline-based quality maps, in: Oil and Gas India Conference and Exhibition. Mumbai.
    [Google Scholar]
  17. Van Essen, G.M., Van den Hof, P.M.J., Jansen, J.D.
    , 2011. Hierarchical Long-Term and Short-Term Production Optimization. SPE J.1, 191–199. doi:10.2118/124332‑PA
    https://doi.org/10.2118/124332-PA [Google Scholar]
  18. Wang, C., Li, G., Reynolds, A.C.
    , 2007. Production optimization in closed-loop reservoir management, in: SPE Annual Technical Conference and Exhibition. SPE 109805, Anaheim, California, USA. doi:DOI: 10.2118/109805‑MS
    https://doi.org/10.2118/109805-MS [Google Scholar]
  19. Zandvliet, M., Handels, M., van Essen, G., Brouwer, R., Jansen, J.-D.
    , 2008. Adjoint-Based Well-Placement Optimization Under Production Constraints. SPE J.13, 26–28. doi: 10.2118/105797-PA
    [Google Scholar]
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