History matching a field case using the ensemble Kalman filter with covariance localization

SPE Reservoir Evaluation and Engineering

Volumn 14, Issue 4, 2011, Pages 443-452

  Emerick, Alexandre A ^a   Reynolds, Albert C ^a  

^a UNIVERSITY OF TULSA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; FORECASTING; ITERATIVE METHODS; MATCHED FILTERS; PETROLEUM RESERVOIRS;

COMPUTATIONAL COSTS; COMPUTATIONAL TIME; ENSEMBLE KALMAN FILTER; FUTURE PERFORMANCE; MATCHING PRODUCTION; PERMEABILITY FIELDS; PREDICTIVE CAPABILITIES; RESERVOIR DESCRIPTION;

KALMAN FILTERS;

COMPUTER SIMULATION; COVARIANCE ANALYSIS; DATA ASSIMILATION; FIELD METHOD; KALMAN FILTER; NUMERICAL MODEL; PREDICTION; RESERVOIR CHARACTERIZATION; VECTOR;

EID: 80051952173     PISSN: 10946470     EISSN: None     Source Type: Journal    
DOI: 10.2118/141216-pa     Document Type: Conference Paper

References (29)

1. Aanonsen, S.I., Nævdal, G., Oliver, D.S., Reynolds, A.C., and Valles, B. 2009. The Ensemble Kalman Filter In Reservoir Engineering - a Review. SPE J. 14 (3): 393-112. SPE-117274-PA. http://dx.doi.org/10.2118/117274-PA.
2. Anderson, J.L. 2007. Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D: Nonlinear Phenomena 230 (1-2): 99-111. http://dx.doi.org/10.1016/j.physd.2006.02.011. (Pubitemid 46776901)
3. Arroyo-Negrete, E., Devegowda, D., Datta-Gupta, A., and Choe, J. 2008. Streamline-Assisted Ensemble Kalman Filter for Rapid and Continuous Reservoir Model Updating. SPE Res Eval & Eng 11 (6): 1046-1060. SPE-104255-PA. http://dx.doi.org/10.2118/104255-PA.
4. Bianco, A., Cominelli, A., Dovera, L., Navdal, G., and Valles, B. 2007. History Matching and Production Forecast Uncertainty by Means of the Ensemble Kalman Filter: A Real Field Application. Paper SPE 107161 presented at the EAGE/EUROPEC Conference and Exhibition, London, 11-14 June. http://dx.doi.org/ 10.2118/107161-MS.
5. Bruhn, C.H.L., Gomes, J.A.T., Luchesse, C.D. Jr., and Johann, P.R.S. 2003. Campos Basin: Reservoir Characterization and Management Historical Overview and Future Challenges. Paper SPE 15220 presented at the Offshore Technology Conference, Houston, 5-8 May. http://dx.doi.org/10.4043/15220-MS.
6. Burgers, G., van Leeuwen, P.J., and Evensen, G. 1998. Analysis Scheme in the Ensemble Kalman Filter. Monthly Weather Review 126 (6): 1719-1724. http://dx.doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2. (Pubitemid 128597660)
7. Chen, Y. and Oliver, D.S. 2010. Cross-covariances and localization for EnKF in multiphase flow data assimilation. Computational Geosci-ences 14 (4): 579-601. http://dx.doi.org/10.1007/s10596-009-9174-6.
8. Damiani, M.C. 2007. Determinação de Padrões de Fluxo em Simulações de Reservatório de Petróleo Utilizando Traçadores (in Portuguese). MSc thesis, COPPE-Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil (May 2007).
9. Devegowda, D., Arroyo, E., Datta-Gupta, A., and Douma, S.G. 2007. Efficient and Robust Reservoir Model Updating Using Ensemble Kalman Filter With Sensitivity Based Covariance Localization. Paper SPE 106144 presented at the SPE Reservoir Simulation Symposium, Houston, 26-28 February. http://dx.doi.org/10. 2118/106144-MS.
10. Emerick, A.A. and Reynolds, A.C. 2010. Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications. Computational Geosciences (in press; published online 16 July 2010). Computational Geosciences (16 July 2010). http://dx.doi.org/10.1007/s10596-010-9198-y.
11. Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. of Geophysical Research 99 (C5): 10143-10162.
12. Evensen, G. 2007. Data Assimilation: The Ensemble Kalman Filter. Berlin: Springer Verlag.
13. Evensen, G. 2009. The ensemble Kalman filter for combined state and parameter estimation. IEEE Control Systems Magazine 29 (3): 83-104. http://dx.doi.org/10.1109/MCS.2009.932223.
14. Furrer, R. and Bengtsson, T. 2007. Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. J. of Multivariate Analysis 98 (2): 227-255. http://dx.doi.org/10.1016/j.jmva.2006.08.003. (Pubitemid 44660963)
15. Gaspari, G. and Cohn, S.E. 1999. Construction of correlation functions in two and three dimensions (DAO Office Note 96-03R1). Quart. J. Roy. Meteor. Soc. 125 (554): 723-757. http://dx.doi.org/10.1002/qj.49712555417. (Pubitemid 29247364)
16. Gu, Y. and Oliver, D.S. 2007. An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation. SPE J. 12 (4): 438-446. SPE-108438-PA. http://dx.doi.org/10.2118/108438-PA.
17. Haugen, V., Nævdal, G., Natvik, L.-J., Evensen, G., Berg, A.M., and Flornes, K.M. 2008. History Matching Using the Ensemble Kalman Filter on a North Sea Field Case. SPE J. 13 (4): 382-391. SPE-102430-PA. http://dx.doi.org/10.2118/102430-PA.
18. Houtekamer, P.L. and Mitchell, H.L. 1998. Data Assimilation Using an Ensemble Kalman Filter Technique. Monthly Weather Review 126 (3): 796-811. http://dx.doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>;2.0.CO;2. (Pubitemid 128581567)
19. Houtekamer, P.L. and Mitchell, H.L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Monthly Weather Review 129 (1): 123-137. http://dx.doi.org/10.1175/1520-0493(2001)129<0123:ASEKFF>;2.0. CO;2. (Pubitemid 32180439)
20. IMEX User Guide. 2010. Computer Modelling Group (CMG).
21. Johann, P., Sansonowski, R., Oliveira, R., and Bampi, D. 2009. 4D seismic in heavy-oil, turbidite reservoir offshore Brazil. The Leading Edge 28 (6): 718-729. http://dx.doi.org/10.1190/L3148415.
22. Li, G. and Reynolds, A.C. 2009. Iterative Ensemble Kalman Filters for Data Assimilation. SPE J. 14 (3): 496-505. SPE-109808-PA. http://dx.doi.org/10. 2118/109808-PA.
23. Lorenzatto, R.A., Juiniti, R., Gomes, J., and Martins, J. 2004. The Marlim Field Development: Strategies and Challenges. Paper OTC 16574 presented at the Offshore Technology Conference, Houston, 3-6 May. http://dx.doi.org/10. 4043/16574-MS.
24. Oliveira, R.M., Bampi, D., Sansonowski, R.C., Junior, N.M.S.R., Johann, P.R.S., Santos, M.S., and Ferreira, D.M. 2007. Marlim Field: Incorporating 4D Seismic in the Geological Model and Application in Reservoir Management Decisions. Paper 108062 presented at the Latin American & Caribbean Petroleum Engineering Conference, Buenos Aires, 15-18 April. http://dx.doi.org/10.2118/108062-MS.
25. Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. 2007. Numerical Recipes: The Art of Scientific Computing, third edition. New York City: Cambridge University Press.
26. Reynolds, A.C., Zafari, M., and Li, G. 2006. Iterative Forms of the Ensemble Kalman Filter. Proc., European Conference on the Mathematics of Oil Recovery, Amsterdam, 4-7 September, Paper A030.
27. Seiler, A., Evensen, G., Skjervheim, J.-A., and Vabø, J.G. 2009. Advanced Reservoir Management Workflow Using and EnKF Based Assisted History Matching Method. Paper SPE 118906 presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA, 2-4 February. http://dx.doi.org/10.2118/ 118906-MS.
28. Thulin, K., Li, G., Aanonsen, S.I., and Reynolds, A.C. 2007. Estimation of Initial Fluid Contacts by Assimilation of Production Data With EnKF. Paper SPE 109975 presented at the SPE Annual Technical Conference and Exhibition, Anaheim, California, USA, 11-14 November. http://dx.doi.org/10.2118/109975-MS.
29. Wang, Y., Li, G., and Reynolds, A.C. 2010. Estimation of Depths of Fluid Contacts and Relative Permeability Curves by History Matching Using Iterative Ensemble-Kalman Smoothers. SPE J. 15 (2): 509-525. SPE-119056-PA. http://dx.doi.org/10.2118/119056-PA.