1887

Abstract

Summary

A geologically plausible history matching workflow has been applied to a complex reservoir to improve reservoir characterization. Different structural interpretations have also been included in the formulation which allow with a single workflow to match petrophysical properties, structural interpretations, fluid properties and fault transmissibilities avoiding any regional multipliers or inconsistent discontinuities. A multiobjective optimization was formulated to assimilate production and pressure timeseries as well as well test data. An inhouse implementation of a Particle Swarm Optimization allows to efficiently solve the optimization problem and provide multiple matching solutions for an improved uncertainty quantification. The multiobjective formulation allows the decision maker to screen the matching realizations based on the degree of confidence on the difference data type as well have better control selected the most representative realizations. The workflow proposed shows good match with the observed quantities and allows a review of the initial model of the field based on the improved understanding of the dynamic response of the reservoir. It is the first step before a field development plan optimization with structural uncertainty.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201802138
2018-09-03
2024-04-27
Loading full text...

Full text loading...

References

  1. CharlesAudet and J. E.Dennis, Jr.
    (2006) Mesh Adaptive Direct Search Algorithms for Constrained Optimization SIAM Journal on Optimization, 2006, Vol. 17, No. 1: pp. 188–217
    [Google Scholar]
  2. Basu, S., Das, A., Paola, G. D., Ciaurri, D. E., Droz, S. E., Llano, C. I., Torrado, R. R.
    (2006). Multi-Start Method for Reservoir Model Uncertainty Quantification with Application to Robust Decision-Making. International Petroleum Technology Conference. doi:10.2523/IPTC‑18711‑MS
    https://doi.org/10.2523/IPTC-18711-MS [Google Scholar]
  3. De PaolaG., TorradoR. R., EmbidS.
    (2014), New Approach for the Generation of the Geological Conceptual Model with Limited Information, Understanding our Green Fields, AAPG International Conference & Exhibition, Istanbul, Turkey
    [Google Scholar]
  4. Embid, S., De Paola, G., Torrado, R. R., Bhattacharjya, D., & Mello, U.
    (2013) Generation of an Accurate Conceptual Model for Green Fields. Society of Petroleum Engineers. doi:10.2118/166500‑MS
    https://doi.org/10.2118/166500-MS [Google Scholar]
  5. Gao, G., Vink, J. C., Chen, C., Tarrahi, M., & El Khamra, Y.
    (2016) Uncertainty Quantification for History Matching Problems With Multiple Best Matches Using a Distributed Gauss-Newton Method. Society of Petroleum Engineers. doi:10.2118/181611‑MS
    https://doi.org/10.2118/181611-MS [Google Scholar]
  6. Isebor, O. J.
    (2013). Derivative-Free Generalized Field Development Optimization. Society of Petroleum Engineers. doi:10.2118/167633‑STU
    https://doi.org/10.2118/167633-STU [Google Scholar]
  7. LewisLi, JefCaers, and PaulSava
    (2015).”Assessing seismic uncertainty via geostatistical velocity-model perturbation and image registration: An application to subsalt imaging.” The Leading Edge, 34(9), 1064–1066, 1068–1070.
    [Google Scholar]
  8. Mohamed, L., Christie, M. A., Demyanov, V., Robert, E., & Kachuma, D.
    (2010). Application of Particle Swarms for History Matching in the Brugge Reservoir. Society of Petroleum Engineers. doi:10.2118/135264‑MS
    https://doi.org/10.2118/135264-MS [Google Scholar]
  9. National Research Council
    . [2010]. Review of the Department of Homeland Security’s Approach to Risk Analysis. Appendix A: Characterization of Uncertainty. Washington, DC: The National Academies Press.
    [Google Scholar]
  10. Torrado, R. R., De Paola, G., Perez, A. F., Fuenmayor, A. A. R., De Azevedo, M. S., & Embid, S.
    (2015) Optimize a WAG Field Development Plan, Use Case of Carbonate Ultra-Deep Water Reservoir. Society of Petroleum Engineers. doi:10.2118/174344‑MS
    https://doi.org/10.2118/174344-MS [Google Scholar]
  11. TorradoR. R, De PaolaG., S.Embid
    , (2014) New Approach for the Generation of the Geological Conceptual Model with Limited Information, Understanding Green Fields, Tu G105 07, 76th EAGE Conference & Exhibition, Amsterdam, Netherlands
    [Google Scholar]
  12. Seiler, A., S.I.Aanonsen, G.Evensen, and J.C.Rivenaes
    , (2010), Structural Surface Uncertainty Modeling and Updating Using the Ensemble Kalman Filter: SPE Journal, v. 15/4, SPE-125352-PA, p. 1062–1076.
    [Google Scholar]
  13. Thore, P., and A.Shtuka
    , (2008), Integration of Structural Uncertainties into Reservoir Grid Construction: Proceedings, EAGE Conference and Exhibition, Rome, Paper I022.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201802138
Loading
/content/papers/10.3997/2214-4609.201802138
Loading

Data & Media loading...

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