Parameterization techniques to improve mass conservation and data assimilation for ensemble Kalman filter

Society of Petroleum Engineers Western North American Regional Meeting 2010 - In Collaboration with the Joint Meetings of the Pacific Section AAPG and Cordilleran Section GSA

Volumn 2, Issue , 2010, Pages 853-865

  Chen, Yan ^a   Oliver, Dean S ^b  

^a Research Partnership to Secure Energy for America   (United States)

^b UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INVERSE PROBLEMS; KALMAN FILTERS; PARAMETERIZATION; PETROLEUM RESERVOIR ENGINEERING;

AUTOMATIC HISTORY MATCHING; CONSERVATION OF MASS; ENSEMBLE KALMAN FILTER; NON-LINEAR CONSTRAINTS; PARAMETERIZATION TECHNIQUES; RELATIVE PERMEABILITY CURVES; RESERVOIR DESCRIPTION; WEIGHTING COEFFICIENT;

RESERVOIR MANAGEMENT;

EID: 77958493435     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2118/133560-ms     Document Type: Conference Paper

References (22)

1. Agbalaka, C. and D. S. Oliver, Application of the EnKF and localization to automatic history matching of fades distribution and production data, Mathematical Geosciences, 40(4), 353-374, 2008.
2. Agbalaka, C. and D. S. Oliver, Parameterization of vertical transmissibility barriers for history matching using EnKF, Mathematical Geosciences, submitted, 2010.
3. Chen, Y. and D. S. Oliver, Ensemble-based closed-loop optimization applied to Brugge Field (SPE 118926), SPE Reservoir Evaluation & Engineering, 13(1), 56-71, 2010.
4. Chen, Y., D. S. Oliver, and D. Zhang, Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization, Journal of Petroleum Science and Engineering, 66, 1-14, 2009.
5. Cominelli, A., L. Dovera, S. Vimercati, and G. Naevdal, Benchmark study of ensemble Kalman filter methodology: history matching and uncertainty quantification for a deep-water oil reservoir (IPTC 13748), in International Petroleum Technology Conference, 7-9 December 2009, Doha, Qatar, 2009.
6. Evensen, G., The ensemble Kalman filter: Theoretical formulation and practical implementation, Ocean Dynamics, 53, 343-367, 2003.
7. Gavalas, G. R., P. C. Shah, and J. H. Seinfeld, Reservoir history matching by Bayesian estimation, SPE Journal, 16(6), 337-350, 1976.
8. Gu, Y. and D. S. Oliver, The ensemble Kalman filter for continuous updating of reservoir simulation models, Journal of Energy Resources Technology, 128(1), 79-87, 2006. (Pubitemid 44058071)
9. Haugen, V., G. Naevdal, L.-J. Natvik, G. Evensen, A. M. Berg, and K. M. Flornes, History matching using the ensemble Kalman filter on a North Sea field case, SPE Journal, 13(4), 382-391, 2008.
10. Hunt, R. J., J. Doherty, and M. J. Tonkin, Are models too simple? arguments for increased parameterization, Ground Water, 45(3), 254-262, 2007.
11. Kravaris, C. and J. H. Seinfeld, Identification of parameters in distributed parameter-systems by regularization, SIAM Journal on Control and Optimization, 23(2), 217-241, 1985.
12. Liu, N. and D. S. Oliver, Critical evaluation of the ensemble Kalman filter on history matching of geologic fades, SPE Reservoir Evaluation & Engineering, 8(6), 470-477, 2005. (Pubitemid 43149906)
13. Lorentzen, R. J., K. M. Flornes, and G. Naevdal, History matching channelized reservoirs using the ensemble Kalman filter (IPTC 14020), in International Petroleum Technology Conference, 7-9 December 2009, Doha, Qatar, 2009.
14. Peters, L., R. Arts, G. Brouwer, and C. Geel, Results of the Brugge benchmark study for flooding optimisation and history matching, SPE-119094, in Proc. of the SPE Reservoir Simulation Symposium, 2-4 February 2009, The Woodlands, 2009.
15. Phale, H. A. and D. S. Oliver, Data assimilation using the constrained ensemble Kalman filter (SPE 125101), in SPE Annual Technical Conference, 4~7 October 2009, New Orleans, Louisiana, U.S.A., 2009.
16. Sarma, P. and W. Chen, Generalization of the ensemble Kalman filter using kernels for non-gaussian random fields, SPE-119177, in SPE Reservoir Simulation Symposium, 2-4 February, The Woodlands, Texas, USA, 2009.
17. Seiler, A., G. Evensen, J. A. Skjervheim, J. Hove, and J. G. Vabo, Advanced reservoir management workflow using an EnKF based assisted history matching method, (SPE 118906), in SPE Reservoir Simulation Symposium, 2-4 February, The Woodlands, Texas, USA, 2009a.
18. Seiler, A., J. Rivenaes, S. Aanonsen, and G. Evensen, Structural uncertainty modelling and updating by production data integration, (SPE 125352), in SPE/EAGE Reservoir Characterization and Simulation Conference, 19-21 October, Abu Dhabi, UAE, 2009b.
19. Skjervheim, J.-A., G. Evensen, S. I. Aanonsen, B. O. Ruud, and T. A. Johansen, Incorporating 4D seismic data in reservoir simulation models using ensemble Kalman filter, SPE Journal, 12(3), 282-292, 2007.
20. Wang, Y., G. Li, and A. C. Reynolds, Estimation of depths of fluid contacts by history matching using iterative ensemble Kalman smoothers (SPE 119056), SPE Journal, accepted, 2010.
21. Zhang, Y. and D. S. Oliver, History matching using a multiscale stochastic model with the ensemble Kalman filter: a field case study, SPE-118879, SPE Journal, minor revision, 2010.
22. Zhao, Y., A. C. Reynolds, and G. Li, Generating fades maps by assimilating production data and seismic data with the ensemble Kalman filter, SPE-113990, in Proceedings of the 2008 SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, April 21-23, 2008.