History Matching the Full Norne Field Model Using Seismic and Production Data
- Rolf J. Lorentzen (IRIS) | Xiaodong Luo (IRIS) | Tuhin Bhakta (IRIS) | Randi Valestrand (IRIS)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2019
- Document Type
- Journal Paper
- 1,452 - 1,467
- 2019.Society of Petroleum Engineers
- Data assimilation, Norne model, Petroelastic model, Iterative ensemble smoother, Correlated noise
- 7 in the last 30 days
- 284 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 35.00|
In this paper, we use a combination of acoustic impedance and production data for history matching the full Norne Field. The purpose of the paper is to illustrate a robust and flexible work flow for assisted history matching of large data sets. We apply an iterative ensemble-based smoother, and the traditional approach for assisted history matching is extended to include updates of additional parameters representing rock clay content, which has a significant effect on seismic data. Further, for seismic data it is a challenge to properly specify the measurement noise, because the noise level and spatial correlation between measurement noise are unknown. For this purpose, we apply a method based on image denoising for estimating the spatially correlated (colored) noise level in the data. For the best possible evaluation of the workflow performance, all data are synthetically generated in this study. We assimilate production data and seismic data sequentially. First, the production data are assimilated using traditional distance-based localization, and the resulting ensemble of reservoir models is then used when assimilating seismic data. This procedure is suitable for real field applications, because production data are usually available before seismic data. If both production data and seismic data are assimilated simultaneously, the high number of seismic data might dominate the overall history-matching performance.
The noise estimation for seismic data involves transforming the observations to a discrete wavelet domain. However, the resulting data do not have a clear spatial position, and the traditional distance-based localization schemes used to avoid spurious correlations and underestimated uncertainty (because of limited ensemble size), are not possible to apply. Instead, we use a localization scheme that is based on correlations between observations and parameters that does not rely on physical position for model variables or data. This method automatically adapts to each observation and iteration.
The results show that we reduce data mismatch for both production and seismic data, and that the use of seismic data reduces estimation errors for porosity, permeability, and net-to-gross ratio (NTG). Such improvements can provide useful information for reservoir management and planning for additional drainage strategies.
|File Size||1 MB||Number of Pages||16|
Bhakta, T., Luo, X., Lorentzen, R., et al. 2017. Estimation of Pressure-Saturation and Porosity Fields From Seismic Acoustic Impedance Data Using an Ensemble Based Method. In SEG International Exposition and 78th Annual Meeting, Houston, Texas, 3107–3112.
Chang, S. G., Yu, B., and Vetterli, M. 2000. Adaptive Wavelet Thresholding for Image Denoising and Compression. IEEE Trans Image Process 9 (9): 1532–1546. https://doi.org/10.1109/83.862633.
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. https://doi.org/10.1007/s10596-013-9351-5.
Chen, Y. and Oliver, D. S. 2014. History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother. SPE Res Eval & Eng 17 (2): 244–256. SPE-164902-PA. https://doi.org/10.2118/164902-PA.
Chen, Y. and Oliver, D. S. 2017. Localization and Regularization for Iterative Ensemble Smoothers. Comput Geosci 21 (1): 13–30. https://doi.org/10.1007/s10596-016-9599-7.
Donoho, D. L. and Johnstone, J. M. 1994. Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika 81 (3): 425–455. https://doi.org/10.1093/biomet/81.3.425.
Emerick, A. and Reynolds, A. 2011. Combining Sensitivities and Prior Information for Covariance Localization in the Ensemble Kalman Filter for Petroleum Reservoir Applications. Comput Geosci 15 (2): 251–269. https://doi.org/10.1007/s10596-010-9198-y.
Emerick, A. A. and Reynolds, A. C. 2013. Ensemble Smoother With Multiple Data Assimilation. Comput & Geosci 55: 3–15 (2013). https://doi.org/10.1016/j.cageo.2012.03.011.
Evensen, G. 2009. Data Assimilation: The Ensemble Kalman Filter, second edition. Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-03711-5_1.
Furrer, R. and Bengtsson, T. 2007. Estimation of High-Dimensional Prior and Posterior Covariance Matrices in Kalman Filter Variants. J Multivar Anal 98 (2): 227–255. https://doi.org/10.1016/j.jmva.2006.08.003.
Gassmann, F. 1951. Über die Elastizität Poröser Medien, Vol. 96, 1–23. Zürich, Switzerland: Vierteljahresschrift der Naturforschenden Gesellschaft.
Hashin, Z. and Shtrikman, S. 1963. A Variational Approach to the Theory of the Elastic Behaviour of Multiphase Materials. J Mech Phys Solids 11 (2): 127–140. https://doi.org/10.1016/0022-5096(63)90060-7.
Houtekamer, P. L. and Mitchell, H. L. 1998. Data Assimilation Using an Ensemble Kalman Filter Technique. Monthly Weather Rev 126: 796–811. https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.
Intel® CoreTM is a trademark of Intel Corporation, 2200 Mission College Blvd., Santa Clara, California, 95054-1549, USA.
Johnstone, I. M. and Silverman, B. W. 1997. Wavelet Threshold Estimators for Data With Correlated Noise. J R Stat Soc: Ser B 59 (2): 319–351. https://doi.org/10.1111/1467-9868.00071.
Lorentzen, R. J. 2017. Norne Initial Ensemble, https://github.com/rolfjl/Norne-Initial-Ensemble (accessed 10 January 2019).
Luo, X. and Bhakta, T. 2017. Estimating Observation Error Covariance Matrix of Seismic Data From a Perspective of Image Denoising. Comput Geosci 21 (2): 205–222. https://doi.org/10.1007/s10596-016-9605-0.
Luo, X., Bhakta, T., Jakobsen, M. et al. 2017. An Ensemble 4D-Seismic History-Matching Framework With Sparse Representation Based on Wavelet Multiresolution Analysis. SPE J. 22 (23): 985–1010. SPE-180025-PA. https://doi.org/10.2118/180025-PA.
Luo, X., Bhakta, T., and Nævdal, G. 2018. Correlation-Based Adaptive Localization With Applications to Ensemble-Based 4D Seismic History Matching. SPE J. 23 (2): 396–427. SPE-185936-PA. https://doi.org/10.2118/185936-PA.
Luo, X., Stordal, A. S., Lorentzen, R. J. et al. 2015. Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem: Theory and Applications. SPE J. 20 (5): 962–982. SPE-176023-PA. https://doi.org/10.2118/176023-PA
MATLAB® is a registered trademark of The MathWorks, Inc., 3 Apple Hill Drive, Natick, Massachusetts 01760, USA.
Mavko, G., Mukerji, T., and Dvorkin, J. 2009. The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media. Cambridge, United Kingdom: Cambridge University Press.
Mindlin, R. D. 1949. Compliance of Elastic Bodies in Contact. J Appl Mech 16: 259–268.
Skjervheim, J. A. 2007. Continuous Updating of a Coupled Reservoir-Seismic Model Using an Ensemble Kalman Filter Technique. PhD thesis, University of Bergen, Bergen, Norway.
Stordal, A. S. 2015. Iterative Bayesian Inversion With Gaussian Mixtures: Finite Sample Implementation and Large Sample Asymptotics. Comput Geosci 19 (1):1–15. https://doi.org/10.1007/s10596-014-9444-9.
Vo, H. X. and Durlofsky, L. J. 2014. A New Differentiable Parameterization Based on Principal Component Analysis for the Low-Dimensional Representation of Complex Geological Models. Math Geosci 46 (7): 775–813. https://doi.org/10.1007/s11004-014-9541-2.
Zhang, Y. and Leeuwenburgh, O. 2017. Image-Oriented Distance Parameterization for Ensemble-Based Seismic History Matching. Comput Geosci 21 (4): 713–731. https://doi.org/10.1007/s10596-017-9652-1.