History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother
- Yan Chen (IRIS) | Dean Stuart Oliver (Uni Research)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- May 2014
- Document Type
- Journal Paper
- 244 - 256
- 2014.Society of Petroleum Engineers
- 5.5.11 Formation Testing (e.g., Wireline, LWD), 5.4.2 Gas Injection Methods, 5.5.8 History Matching
- ensemble smoother, iterative ensemble smoother, history matching, ensemble Kalman filter, Norne field
- 6 in the last 30 days
- 554 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Although ensemble-based data-assimilation methods such as the ensemble Kalman filter (EnKF) and the ensemble smoother have been extensively used for the history matching of synthetic models, the number of applications of ensemble-based methods for history matching of field cases is extremely limited. In most of the published field cases in which the ensemble-based methods were used, the number of wells and the types of data to be matched were relatively small. As a result, it may not be clear to practitioners how a real history-matching study would be accomplished with ensemble-based methods. In this paper, we describe the application of the iterative ensemble smoother to the history matching of the Norne field, a North Sea field, with a moderately large number of wells, a variety of data types, and a relatively long production history. Particular attention is focused on the problems of the identification of important variables, the generation of an initial ensemble, the plausibility of results, and the efficiency of minimization. We also discuss the challenges encountered in the use of the ensemble based method for complex-field case studies that are not typically encountered in synthetic cases. The Norne field produces from an oil-and-gas reservoir discovered in 1991 offshore Norway. The full-field model consists of four main fault blocks that are in partial communication and many internal faults with uncertain connectivity in each fault block. There have been 22 producers and 9 injectors in the field. Water-alternating-gas injection is used as the depletion strategy. Production rates of oil, gas, and water of 22 producers from 1997 to 2006 and repeat-formation-tester (RFT) pressure from 14 different wells are available for model calibration. The full-field simulation model has 22 layers, each with a dimension of 46 x 112 cells. The total number of active cells is approximately 45,000. The Levenberg-Marquardt form of the iterative ensemble smoother (LM-EnRML) is used for history matching. The model parameters that are updated include permeability, porosity, and net-to-gross (ntg) ratio at each gridblock; vertical transmissibility at each gridblock for six layers; transmissibility multipliers of 53 faults; endpoint water and gas relative permeability of four different reservoir zones; depth of water/oil contacts; and transmissibility multipliers between a few main fault blocks. The total number of model parameters is approximately 150,000. Distance-based localization is used to regularize the updates from LM-EnRML. LM-EnRML is able to achieve improved data match compared with the manually history matched model after three iterations. Updates from LM-EnRML do not introduce artifacts in the property fields as in the manually history- matched model. The automated workflow is also much less labor-intensive than that for manual history matching.
|File Size||2 MB||Number of Pages||13|
Aanonsen, S.I., Nævdal, G., Oliver, D.S. et al. 2009. Ensemble Kalman Filter in Reservoir Engineering—A Review. SPE J. 14 (3): 393–412. http://dx.doi.org/10.2118/117274-PA.
Arroyo-Negrete, E., Devegowda, D., Datta-Gupta, A. et al. 2008. Streamline-Assisted Ensemble Kalman Filter for Rapid and Continuous Reservoir Model Updating. SPE Res Eval & Eng 11 (6): 1046–1060. http://dx.doi/10.2118/104255-PA.
Becerra, G.G., Modenesi, A.P., and Lisboa, E.F.A. 2012. Uncertainty History Matching and Forecasting, A Field Case Application. Paper SPE 153176 presented at the SPE Latin America and Caribbean Petroleum Engineering Conference, Mexico City, Mexico, 16–18 April. http://dx.doi.org/10.2118/153176-MS.
Bianco, A., Cominelli, A., Dovera, L. et al. 2007. History Matching and Production Forecast Uncertainty by Means of the Ensemble Kalman Filter: A Real Field Application. Paper SPE107161 presented at the SPE Europec/EAGE Annual Conference and Exhibition, London, United Kingdom, 11–14 June. http://dx.doi.org/10.2118/107161-MS.
Burgers, G., van Leeuwen, P.J., and Evensen. G. 1998. Analysis Scheme in the Ensemble Kalman Filter. Mon. Weather Rev. 126 (6): 1719–1724. http://dx.doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.
Chen, Y. and Oliver, D.S. 2010a. Ensemble-Based Closed-Loop Optimization Applied to Brugge Field. SPE Res Eval & Eng 13 (1): 56–71. http://dx.doi.org/10.2118/118926-PA.
Chen, Y. and Oliver, D.S. 2010b. Parameterization Techniques To Improve Mass Conservation and Data Assimilation for Ensemble Kalman Filter. Paper SPE 133560 presented at the SPE Western Regional Meeting, Anaheim, California, 27–29 May. http://dx.doi.org/10.2118/133560-MS.
Chen, Y. and Oliver, D.S. 2012. Ensemble Randomized Maximum Likelihood Method As an Iterative Ensemble Smoother. Math. Geosci. 44 (1): 1–26. http://dx.doi.org/10.1007/s11004-011-9376-z.
Chen, Y. and Oliver, D.S. 2013. Levenberg-Marquardt Forms of the Iterative Ensemble Smoother for Efficient History Matching and Uncertainty Quantification. Comput. Geosci. 17 (4): 689–703. http://dx.doi.org/10.1007/s10596-013-9351-5.
Cominelli, A., Dovera, L., Vimercati, S. et al. 2009. Benchmark Study of Ensemble Kalman Filter Methodology: History Matching and Uncertainty Quantification for a Deep-Water Oil Reservoir. Paper IPTC 13748 presented at the International Petroleum Technology Conference, Doha, Qatar, 7–9 December. http://dx.doi.org/10.2523/13748.
Emerick, A.A. and Reynolds, A.C. 2011. History Matching a Field Case Using the Ensemble Kalman Filter With Covariance Localization. SPE Res Eval Eng 14 (4): 443–452. http://dx.doi.org/10.2118/141216-PA.
Evensen, G. 1994. Sequential Data Assimilation With a Nonlinear Quasi-Geostrophic Model Using Monte Carlo Methods To Forecast Error Statistics. J. Geophys. Res. 99 (C5): 10143–10162.
Evensen, G., Hove, J., Meisingset, H.C. et al. 2007. Using the EnKF for Assisted History Matching of a North Sea Reservoir Model. Paper SPE 106184 presented at the 2007 SPE Reservoir Simulation Symposium, The Woodlands, Texas, 26–28 February. http://dx.doi.org/10.2118/106184-MS.
Haugen, V., Nævdal, G., Natvik, L.-J. et al. 2008. History Matching Using the Ensemble Kalman Filter on a North Sea Field Case. SPE J. 13 (4): 382–391. http://dx.doi.org/10.2118/102430-PA.
Huseby, O. 2005. Norne reservoir simulation model, updated reference case 2005. Technical report, Statoil.
Menke, W. 1989. Geophysical Data Analysis: Discrete Inverse Theory, revised edition, San Diego: Academic Press.
Oliver, D.S., Reynolds, A.C., and Liu, N. 2008. Inverse Theory for Petroleum Reservoir Characterization and History Matching, first edition, Cambridge: Cambridge University Press.
Peters, L., Arts, R.J., Brouwer, G.K. et al. 2010. Results of the Brugge Benchmark Study for Flooding Optimization and History Matching. SPE Res Eval Eng 13 (3): 391–405. http://dx.doi.org/10.2118/119094-PA.
Peters, E., Chen, Y., Leeuwenburgh, O. et al. 2013. Extended Brugge Benchmark Case for History Matching and Water Flooding Optimization. Computers & Geosci. 50: 16–24. http://dx.doi.org/10.1016/j.cageo.2012.07.018.
Seiler, A., Evensen, G., Skjervheim, J.A. et al. 2009. Advanced Reservoir Management Workflow Using an EnKF Based Assisted History Matching Method. Paper SPE 118906 presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2–4 February. http://dx.doi.org/10.2118/118906-MS.
Skjervheim, J.-A., Evensen, G., Aanonsen, S.I. et al. 2007. Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter. SPE J. 12 (3): 282–292. http://dx.doi.org/10.2118/95789-PA.
Skjervheim, J.-A. and Evensen, G. 2011. An Ensemble Smoother for Assisted History Matching. Paper SPE 141929 presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 21–23 February. http://dx.doi.org/10.2118/141929-MS.
Tarantola, A. 2005. Inverse Problem Theory and Methods for Model Parameter Estimation. Society for Industrial and Applied Mathematics.
Zhang, Y. and Oliver, D.S. 2011. History Matching Using a Multiscale Stochastic Model With the Ensemble Kalman Filter: A Field Case Study. SPE J. 16 (2): 307–317. http://dx.doi.org/10.2118/118879-PA.