A parallel ensemble-based framework for reservoir history matching and uncertainty characterization

Computational Geosciences

Volumn 17, Issue 1, 2013, Pages 83-97

  Tavakoli, Reza ^a   Pencheva, Gergina ^a   Wheeler, Mary F ^a   Ganis, Benjamin ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Automatic history matching; Ensemble Kalman filter; Ensemble smoother; Parallel efficiency

Indexed keywords

COMPUTER SIMULATION; DATA ASSIMILATION; ENSEMBLE FORECASTING; KALMAN FILTER; NUMERICAL MODEL; RESERVOIR; SMOOTHING;

EID: 84872265279     PISSN: 14200597     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10596-012-9315-1     Document Type: Article

References (36)

1. Aanonsen, S. I., Nævdal, G., Oliver, D. S., Reynolds, A. C., Vallès, B.: The ensemble Kalman filter in reservoir engineering-a review. SPE J. 14(3), 393-412 (2009). doi: 10. 2118/117274-PA.
2. Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., Avellano, A.: The data assimilation research testbed: a community facility. Bull. Am. Meteorol. Soc. 90, 1283-1296 (2009). doi: 101175/2009BAMS26181.
3. Eisenstat, S., Walker, H.: Choosing the forcing terms in an inexact Newton method. SIAM J. Sci. Comput. 17(1), 16-32 (1996).
4. Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99(C5), 10143-10162 (1994).
5. Evensen, G.: Sampling strategies and square root analysis schemes for the EnKF. Ocean Dyn. 54(6), 539-560 (2004).
6. Evensen, G.: The ensemble kalman filter for combined state and parameter estimation. IEEE Control Syst. Mag. 29(3), 83-104 (2009).
7. Emerick, A. A., Reynolds, A.: Ensemble smoother with multiple data assimilation. Comput. Geosci. (2012). doi: 101016/j. cageo. 2012. 03. 011.
8. Evensen, G.: Data Assimilation: The Ensemble Kalman Filter. Springer, Berlin (2007).
9. Evensen, G., van Leeuwen, P. J.: An ensemble Kalman smoother for nonlinear dynamics. Mon. Weather Rev. 128(6), 1852-1867 (2000).
10. Evensen, G., Hove, J., Meisingset, H. C., Reiso, E., Seim, K. S., Espelid, O.: Using the EnKF for assisted history matching of a North Sea reservoir model (SPE 106184). In: Proceedings of the 2007 SPE Reservoir Simulation Symposium Houston, Texas, 26-28 February 2007.
11. Gao, G., Zafari, M., Reynolds, A. C.: Quantifying uncertainty for the PUNQ-S3 problem in a Bayesian setting with RML and EnKF. SPE J. 11(4), 506-515 (2006).
12. Gu, Y., Oliver, D. S.: History matching of the PUNQ-S3 reservoir model using the ensemble Kalman filter. SPE J. 10(2), 51-65 (2005).
13. Houtekamer, P. L., Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 129(1), 123-137 (2001).
14. Kalman, R. E.: A new approach to linear filtering and prediction problems. J. Basic Eng-T ASME 82, 35-45 (1960).
15. Keppenne, C. L.: Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter. Mon. Weather Rev. 128(6), 1971-1981 (2000).
16. Keppenne, C. L., Rienecker, M. M.: Initial testing of a massively parallel ensemble Kalman filter with the Poseidon isopycnal ocean general circulation model. Mon. Weather Rev. 130(12), 2951-2965 (2002).
17. Lacroix, S., Vassilevski, Y., Wheeler, M.: Iteretive solvers of the implicit parallel accurate reservoir simulator (IPARS). I: Single processor case. TICAM report 00-28, The University of Texas at Austin, Austin (2000).
18. Lacroix, S., Vassilevski, Y., Wheeler, M.: Decoupling preconditioners in the implicit parallel accurate reservoir simulator (IPARS). Numer. Linear Algebra Appl. 8(8), 537-549 (2001).
19. Lacroix, S., Vassilevski, Y., Wheeler, J., Wheeler, M.: Iterative solution methods for modeling multiphase flow in porous media fully implicitly. SIAM J. Sci. Comput. 25(3), 905-926 (2003).
20. van Leeuwen, P. J., Evensen, G.: Data assimilation and inverse methods in terms of a probabilistic formulation. Mon. Weather Rev. 124, 2898-2913 (1996).
21. Liang, B., Sepehrnoori, K., Delshad, M.: An automatic history matching module with distributed and parallel computing. Pet. Sci. Technol. 27(10), 1092-1108 (2009).
22. Lu, B., Wheeler, M. F.: Iterative coupling reservoir simulation on high performance computers. Pet. Sci. 6, 43-50 (2009).
23. Nævdal, G., Mannseth, T., Vefring, E. H.: Near-well reservoir monitoring through ensemble Kalman filter: SPE 75235. In: Proceedings of SPE/DOE Improved Oil Recovery Symposium Freiberg, Germany, 3-6 September (2002).
24. Nævdal, G., Johnsen, L. M., Aanonsen, S. I., Vefring, E. H.: Reservoir monitoring and continuous model updating using ensemble Kalman filter. SPE 84372 (2003).
25. Nerger, L., Hiller, W., Schrter, J.: PDAF-the parallel data assimilation framework: experiences with Kalman filtering. In: Zwieflhofer, W., Mozdzynski, G. (eds.) Use of High Performance Computing in Meteorology-Proceedings of the 11 ECMWF Workshop, pp. 63-83. World Scientific, Singapore (2005).
26. Raeder, K., Anderson, J., Collins, N., Hoar, T. J., Kay, J. E.: Dart/cam: an ensemble data assimilation system for cesm atmospheric models. J. Climate (American Meteorological Society) (2012) doi: 10. 1175/JCLI-D-11-00395. 1.
27. Serafy, G. E., Verlaan, M., Hummel, S., Weerts, A., Dhondia, J.: Openda open source generic data assimilation environment and its application in process models. Geophys. Res. Abstr. 12, EGU2010-9346-2 (2010).
28. SESAM: An integrated system of sequential assimilation modules (SESAM). URL http://www-meomhmginpgfr/Web/Outils/SESAM/ (2009). Accessed 10 Aug 2012.
29. Skjervheim, J. A., Evensen, G., Aanonsen, S. I., Ruud, B. O., Johansen, T. A.: Incorporating 4D seismic data in reservoir simulation models using ensemble Kalman filter. SPE J. 12(3), 282-292 (2007). doi: 10. 2118/95789-PA.
30. Skjervheim, J. A., Evensen, G., Hove, J., Vabo, J. G.: An ensemble smoother for assisted history matching. SPE 141929, presented at the SPE Reservoir Simulation Symposium, 21-23 February 2011.
31. Tavakoli, R., Pencheva, G., Wheeler, M. F.: Multi-level parallelization of ensemble kalman filter for reservoir history matching. SPE 141657-PP, presented at the SPE Reservoir Simulation Symposium, 21-23 February 2011.
32. Tavakoli, R., Pencheva, G., Wheeler, M. F., Ganis, B.: Petroleum reservoir parameter estimation and uncertainty assessment with the parallel ensemble based framework coupled with ipars. Tech. rep., The Institute of Computational Engineering and Sciences (ICES), The Univeristy of Texas at Austin (2011).
33. Vassilevski, Y.: Iterative solvers for the implicit parallel accurate reservoir simulator (IPARS). II: Parallelization issues. TICAM Report 00-33, The University of Texas at Austin (2000).
34. Wheeler, J., et al.: IPARS User's Manual. Center for Subsurface Modeling, ICES, The University of Texas at Austin (2002).
35. Wheeler, M. F.: Advanced techniques and algorithms for reservoir simulation, II: the multiblock approach in the integrated parallel accurate reservoir simulator (IPARS). Tech. Rep. 01-25, ICES, The University of Texas at Austin, Austin, TX (2001).
36. Xin, L.: Continuous reservoir model updating by ensemble Kalman filter on grid computing architectures. Ph. D. thesis, Louisiana State University, Baton Rouge, Louisiana (2008).