1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. ECMOR XVII
  5. Conference paper

Abstract

Summary

Field development planning using reservoir models is a key step in the field development process. Numerical optimisation of specific field development strategies is often used to aid planning. Bayesian Optimisation is a popular optimisation method that has previously been applied to this problem. However, reservoir models can have a high degree of geological uncertainty associated with them, even after history matching. It is important to be able to perform optimisation that accounts for this uncertainty. To date, limited attention has been given to Bayesian Optimisation of field development strategies under geological uncertainty.

Much of the recent work in this area has focused on Ensemble Optimisation methods. These naturally handle geological uncertainty using ensembles of geological realisations. This can result in a high computational cost, as large ensembles are required to capture the geological uncertainty. Bayesian Optimisation offers an alternative solution using probabilistic surrogate or proxy models that can capture the geological uncertainty. However, incorporating geological uncertainty into proxy models and using those models in a Bayesian Optimisation loop remains a challenging task. Further, the effect of the additional proxy model uncertainty on optimisation results has not been well studied.

We propose a Bayesian Optimisation workflow comprising a Stochastic Bayes Linear proxy model and a combination of experimental and sequential design techniques. The workflow is designed to include a combination of static and dynamic uncertainties, with a new geological realisation generated and used to simulate fluid flow during each run of the model. The workflow is demonstrated by optimising several field development strategies in a synthetic North Sea reservoir model. The ability of the workflow to locate optima and correctly account for the geological uncertainty is studied and the computational cost is quantified.

The performance and practical implications of the proposed approach are discussed. These are important in designing an accurate and computationally efficient optimisation workflow under geological uncertainty and, ultimately, are factors in developing decision support tools for field development.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202035121
2020-09-14
2024-04-19

  • From This Site

    /content/papers/10.3997/2214-4609.202035121
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aarnes, I., Midtveit, K. & Skorstad, A.
    , 2015. Evergreen workflows that capture uncertainty – the benefits of an unlocked structure. First Break, 33(10), pp. 89–92.
     [Google Scholar]
  2. Alrashdi, Z. & Sayyafzadeh, M.
    , 2019. (μ+λ) Evolution strategy algorithm in well placement, trajectory, control and joint optimisation. Journal of Petroleum Science and Engineering, Volume 177, pp. 1042–1058.
     [Google Scholar]
  3. Andrianakis, I.
    , 2016. History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation. Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 66.
     [Google Scholar]
  4. , 2015. Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda. PLOS Computational Biology, 1, Volume 11, pp. 1–18.
     [Google Scholar]
  5. Ankenman, B., Nelson, B. L. & Staum, J.
    , 2010. Stochastic Kriging for Simulation Metamodeling. Operations Research, Volume 58, pp. 371–382.
     [Google Scholar]
  6. Artus, V., Durlofsky, L. J., Onwunalu, J. & Aziz, K.
    , 2006. Optimization of nonconventional wells under uncertainty using statistical proxies. Computational Geosciences, Volume 10, p. 389–404.
     [Google Scholar]
  7. Aslam, U. & Bordas, R.
    , 2020. An Ensemble-Based History Matching Approach for Reliable Production Forecasting from Shale Reservoirs. Accepted to Unconventional Resources Techology Conference.
     [Google Scholar]
  8. Berger, J. O.
    , 2010. Statistical decision theory and Bayesian analysis. 2nd ed. New York: Springer.
     [Google Scholar]
  9. Chen, Y., Oliver, D. S. & Zhang, D.
    , 2009. Efficient Ensemble-Based Closed-Loop Production Optimization. SPE Journal, Volume 14, p. 634–645.
     [Google Scholar]
  10. Ciaurri, D. E., Mukerji, T. & Durlofsky, L. J.
    , 2011. Derivative-Free Optimization for Oil Field Operations. In: X.Yang & S.Koziel, eds. Computational Optimization and Applications in Engineering and Industry. Berlin: Springer, p. 19–55.
     [Google Scholar]
  11. Emerick, A.
    , 2009. Well Placement Optimization Using a Genetic Algorithm With Nonlinear Constraints. SPE Reservoir Simulation Symposium, SPE-118808-MS.
     [Google Scholar]
  12. Evensen, G.
    , 2003. The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, Volume 53, p. 343–367.
     [Google Scholar]
  13. Fillacier, S.
    , 2014. Calculating Prediction Uncertainty using Posterior Ensembles Generated from Proxy Models. SPE Russian Oil and Gas Exploration & Production Technical Conference and Exhibition, SPE-171237-MS, p. 11.
     [Google Scholar]
  14. Fonseca, R. M.
    , 2018. Overview of the olympus field development optimization challenge. 16th European Conference on the Mathematics of Oil Recovery, ECMOR 2018, EAGE.
     [Google Scholar]
  15. Fonseca, R. R.-M., Chen, B., Jansen, J. D. & Reynolds, A.
    , 2017. A Stochastic Simplex Approximate Gradient (StoSAG) for optimization under uncertainty. International Journal for Numerical Methods in Engineering, Volume 109, pp. 1756–1776.
     [Google Scholar]
  16. Ginsbourger, D., Le Riche, R. & Carraro, L.
    , 2008. A Multi-points Criterion for Deterministic Parallel Global Optimization based on Kriging. HAL, 3.
     [Google Scholar]
  17. Goldstein, M.
    , 2012. Bayes Linear Analysis for Complex Physical Systems Modeled by Computer Simulators. Berlin, Springer, p. 78–94.
     [Google Scholar]
  18. Goldstein, M. & Wooff, D.
    , 2007. Bayes Linear Statistics: Theory and Methods. Chichester: John Wiley & Sons Ltd.
     [Google Scholar]
  19. Goodwin, N. H.
    , 2018. Efficient Large Scale Optimization Under Uncertainty With Multiple Proxy Models. EAGE/TNO Workshop on OLYMPUS Field Development Optimization, European Association of Geoscientists & Engineers.
     [Google Scholar]
  20. Hansen, N. & Ostermeier, A.
    , 1996. Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. Proceedings of IEEE International Conference on Evolutionary Computation, IEEE, p. 312–317.
     [Google Scholar]
  21. Jansen, J. D.
    , 2011. Adjoint-based optimization of multi-phase flow through porous media – A review. Computers & Fluids, Volume 46, pp. 40–51.
     [Google Scholar]
  22. Kohonen, T.
    , 1982. Self-organized formation of topologically correct feature maps. Biological Cybernetics, Volume 43, p. 59–69.
     [Google Scholar]
  23. Lorentzen, R. J., Berg, A., Naevdal, G. & Vefring, E. H.
    , 2006. A New Approach For Dynamic Optimization Of Water Flooding Problems. Intelligent Energy Conference and Exhibition, SPE-99690-MS.
     [Google Scholar]
  24. Peters, L.
    , 2010. Results of the Brugge Benchmark Study for Flooding Optimization and History Matching. SPE Reservoir Evaluation & Engineering, Volume 13, p. 391–405.
     [Google Scholar]
  25. Schulte, D. O.
    , 2020. Multi-objective optimization under uncertainty of geothermal reservoirs using experimental design-based proxy models. Geothermics, Volume 86, p. 101792.
     [Google Scholar]
  26. Schwarz, G.
    , 1978. Estimating the Dimension of a Model. Ann. Statist., Volume 6, p. 461–464.
     [Google Scholar]
  27. Shahriari, B.
    , 2016. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE, 1, Volume 104, pp. 148–175.
     [Google Scholar]
  28. Stein, M.
    , 1987. Large Sample Properties of Simulations Using Latin Hypercube Sampling. Technometrics, 5, Volume 29, p. 143–151.
     [Google Scholar]
  29. Taha, T.
    , 2019. History Matching Using 4D Seismic in an Integrated Multi-Disciplinary Automated Workflow. SPE Reservoir Characterisation and Simulation Conference and Exhibition, SPE-196680-MS.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202035121
Loading
A Bayesian Optimisation Workflow for Field Development Planning Under Geological Uncertainty
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202035121
/content/papers/10.3997/2214-4609.202035121
/content/papers/10.3997/2214-4609.202035121
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error