Advertisement

Olympus challenge—standardized workflow design for field development plan optimization under uncertainty

  • Ralf Schulze-RiegertEmail author
  • Michael Nwakile
  • Sergey Skripkin
  • Michelle Whymark
  • James Baffoe
  • Dirk Geissenhoener
  • Adrian Anton
  • Chresten Steen Meulengracht
  • Kin Jin Ng
Original Paper
  • 12 Downloads

Abstract

The “Olympus challenge” is defined as an open benchmark study on field development optimization under geological uncertainty. This work describes a structured approach to the Olympus challenge. It combines a systematic performance delivery analysis based on multiple reservoir model realizations with optimization strategies including well controls, field development scenarios, and combined strategies. The ambition of this work is to design practical and robust workflows integrating economic, reservoir geology, and delivery performance that can be applied to current real field studies. Probabilistic assessments on economic performance and reservoir opportunities are used for project framing and to define start points for optimization strategies. A sequential optimization strategy is applied to handle discrete control parameters with a time-dependent impact on economic performance over the life cycle of the reservoir. Probability maps are applied to identify reservoir opportunities and a probabilistic well ranking is introduced to investigate the robustness of a well location design. Objective measures for probabilistic evaluations are described and applied for result comparison between an optimized and a reference solution in the presence of multiple realizations. In conclusion, this work provides solution proposals for multiple optimization objectives of the Olympus challenge. Standardized workflow designs deliver manageable and repeatable work steps and give an outlook to automation.

Keywords

Olympus challenge Field development Geological uncertainty Uncertainty management Data-driven optimization Dimension reduction Reservoir simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgment

The OLYMPUS benchmark study is an initiative of the partners of the Integrated Systems Approach to Petroleum Production (ISAPP) research consortium consisting of TNO, Delft University of Technology, ENI, Equinor and Petrobras.

The authors of this paper would like to thank the management of Schlumberger for providing resources to complete this work. Conclusions and opinions stated in this paper are those of the authors and do not necessarily represent those of Schlumberger.

References

  1. 1.
    Capolei, A., Christiansen, L., Jørgensen, J.: Offset risk minimization for open-loop optimal control of oil reservoirs. (pp. 10620-10625). IFAC PapersOnLine. 50-1, Elsevier (2017).  https://doi.org/10.1016/j.ifacol.2017.08.1034
  2. 2.
    Chen, Y., Oliver, D.S., Zhang, D.: Efficient ensemble-based closed-loop production optimization. SPE Journal. 14(04), (2009).  https://doi.org/10.2118/112873-PA
  3. 3.
    Chugunov, N. R. (2015). Method for adaptive optimization of EOR performance under uncertainty. Society of Petroleum Engineers. doi:doi: https://doi.org/10.2118/173295-MS
  4. 4.
    Davis, J.C.: Statistics and Data Analysis in Geology. John Wiley & Sons, New York, NY (2002)Google Scholar
  5. 5.
    Erber, A., Erber, D., Meulengracht, C.: Impact of geological and facility uncertainties on strategic drilling and facility decisions. Society of Petroleum Engineers. (2014).  https://doi.org/10.2118/169878-MS
  6. 6.
    Fonseca, R.M., Della Rossa, E., Emerick, A.A., Hanea, R.G., Jansen, J.D.: Overview of the OLYMPUS Field Development Optimization Challenge. ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery. EAGE. (2018).  https://doi.org/10.3997/2214-4609.201802246
  7. 7.
    Hanea, R., Fonseca, R., Pettan, C., Iwajomo, M., Skjerve, K., Hustoft, L., Chitu, A., Wilschut, F.: Decision maturation using ensemble based robust optimization for field development planning. ECMOR XV - 15th European Conference on the Mathematics of Oil Recovery. (2016).  https://doi.org/10.3997/2214-4609.201601872
  8. 8.
    Hastie, T., Tibshirani, R., Friedmann, J.: The Elements of Statistical Learning. Springer, Berlin (2009)CrossRefGoogle Scholar
  9. 9.
    Li, L., Jafarpour, B., Mohammad-Khaninezhad, M.R.: A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty. Computational Geoscience. 17(1), 167–188 (2013).  https://doi.org/10.1007/s10596-012-9323-1
  10. 10.
    Mitsuo, G., Cheng, R.: Genetic Algorithms and Engineering Optimization. John Wiley & Sons, Inc., Hoboken (1999).  https://doi.org/10.1002/9780470172261 CrossRefGoogle Scholar
  11. 11.
    Montgomery, D.C.: Design and Analysis of Experiments. John Wiley & Sons, Inc., Hoboken (2017)Google Scholar
  12. 12.
    Onwunalu, J.E., Durlofsky, L.J.: Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput. Geosci. 14(1), 183–198 (2010).  https://doi.org/10.1007/s10596-009-9142-1 CrossRefGoogle Scholar
  13. 13.
    Suzuki, S., Caumon, G., Caers, J.: Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization. Computational Geoscience. 12(1), 105–119 (2008).  https://doi.org/10.1007/s10596-007-9063-9 CrossRefGoogle Scholar
  14. 14.
    Wang, H., Echeverría Ciaurri, D., Durlofsky, L.J., Cominelli, A.: Optimal well placement under uncertainty using a retrospective optimization framework. SPE J. 17(1), 112–121 (2012).  https://doi.org/10.2118/141950-PA CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ralf Schulze-Riegert
    • 1
    Email author
  • Michael Nwakile
    • 1
  • Sergey Skripkin
    • 1
  • Michelle Whymark
    • 1
  • James Baffoe
    • 1
  • Dirk Geissenhoener
    • 1
  • Adrian Anton
    • 2
  • Chresten Steen Meulengracht
    • 2
  • Kin Jin Ng
    • 3
  1. 1.Schlumberger Norwegian Technology Center, Gamle Borgenveien 3AskerNorway
  2. 2.Schlumberger, Dampfærgevej 27-29CopenhagenDenmark
  3. 3.Schlumberger, 110 Schlumberger Dr.Sugar LandUSA

Personalised recommendations