Abstract
The degrees of freedom (DOF) in standard ensemble-based data assimilation is limited by the ensemble size. Successful assimilation of a data set with large information content (IC) therefore requires that the DOF is sufficiently large. A too small number of DOF with respect to the IC may result in ensemble collapse, or at least in unwarranted uncertainty reduction in the estimation results. In this situation, one has two options to restore a proper balance between the DOF and the IC: to increase the DOF or to decrease the IC. Spatially dense data sets typically have a large IC. Within subsurface applications, inverted time-lapse seismic data used for reservoir history matching is an example of a spatially dense data set. Such data are considered to have great potential due to their large IC, but they also contain errors that are challenging to characterize properly. The computational cost of running the forward simulations for reservoir history matching with any kind of data is large for field cases, such that a moderately large ensemble size is standard. Realization of the potential in seismic data for ensemble-based reservoir history matching is therefore not straightforward, not only because of the unknown character of the associated data errors, but also due to the imbalance between a large IC and a too small number of DOF. Distance-based localization is often applied to increase the DOF but is example specific and involves cumbersome implementation work. We consider methods to obtain a proper balance between the IC and the DOF when assimilating inverted seismic data for reservoir history matching. To decrease the IC, we consider three ways to reduce the influence of the data space; subspace pseudo inversion, data coarsening, and a novel way of performing front extraction. To increase the DOF, we consider coarse-scale simulation, which allows for an increase in the DOF by increasing the ensemble size without increasing the total computational cost. We also consider a combination of decreasing the IC and increasing the DOF by proposing a novel method consisting of a combination of data coarsening and coarse-scale simulation. The methods were compared on one small and one moderately large example with seismic bulk-velocity fields at four assimilation times as data. The size of the examples allows for calculation of a reference solution obtained with standard ensemble-based data assimilation methodology and an unrealistically large ensemble size. With the reference solution as the yardstick with which the quality of other methods are measured, we find that the novel method combining data coarsening and coarse-scale simulations gave the best results. With very restricted computational resources available, this was the only method that gave satisfactory results.
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References
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)
Axelsson, O.: Iterative Solution Methods. Cambridge University Press, Cambridge (1994)
Berre, I., Lien, M., Mannseth, T.: Multi-level parameter structure identification for two-phase porous-media flow problems using flexible representations. Adv. Water Resour. 32(12), 1777–1788 (2009)
Chen, Y., Oliver, D.S.: Levenberg-marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Comput. Geosci. 17(4), 689–703 (2013)
Davolio, A., Maschio, C., Schiozer, D.J.: Pressure and saturation estimation from P and S impedances: A theoretical study. J. Geophys. Eng. 9(5), 447 (2012)
Dong, Y., Gu, Y., Oliver, D.S.: Sequential assimilation of 4D seismic data for reservoir description using the ensemble Kalman filter. J. Pet. Sci. Eng. 53(1), 83–99 (2006)
Durlofsky, L.J.: Upscaling of geocellular models for reservoir flow simulation: a review of recent progress. In: Proc. 7th International Forum on Reservoir Simulation. Bühl/Baden-Baden, Germany (2003)
Emerick, A.A., Reynolds, A.C.: History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations. Comput. Geosci. 16(3), 639–659 (2012)
Emerick, A.A., Reynolds, A.C.: Ensemble smoother with multiple data assimilation. Comput. Geosci. 55, 3–15 (2013)
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. Oceans 99(C5), 10,143–10,162 (1994)
Evensen, G.: Data Assimilation: The Ensemble Kalman Filter. Springer Science & Business Media (2009)
Fahimuddin, A.: 4D seismic history matching using the ensemble Kalman filter (EnKF): possibilities and challenges. Ph.D. Thesis, The University of Bergen (2010)
Farmer, C.L.: Upscaling: A review. Int. J. Numer. Methods Fluids 40(1-2), 63–78 (2002). https://doi.org/10.1002/fld.267
Fossum, K., Mannseth, T.: Coarse-scale data assimilation as a generic alternative to localization. Comput. Geosci. 21(1), 167–186 (2017)
Houtekamer, P.L., Mitchell, H.L.: Data assimilation using an ensemble Kalman filter technique. Mon. Weather. Rev. 126(3), 796–811 (1998)
van Leeuwen, P.J., Evensen, G.: Data assimilation and inverse methods in terms of a probabilistic formulation. Mon. Weather. Rev. 124(12), 2898–2913 (1996)
Leeuwenburgh, O., Arts, R.: Distance parameterization for efficient seismic history matching with the ensemble Kalman filter. Comput. Geosci. 18(3-4), 535–548 (2014)
Leeuwenburgh, O., Brouwer, J., Trani, M.: Ensemble-based conditioning of reservoir models to seismic data. Comput. Geosci. 15(2), 359–378 (2011)
Schlumberger GeoQuest: ECLIPSE 100 reference manual version 2004a (2004)
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)
Trani, M., Arts, R., Leeuwenburgh, O.: Seismic history matching of fluid fronts using the ensemble Kalman filter. SPE J. 18(1), 159–171 (2012)
Wang, Y., Li, G., Reynolds, A.C.: Estimation of depths of fluid contacts by history matching using iterative ensemble-Kalman smoothers. SPE J. 15(02), 509–525 (2010)
Zhang, Y., Leeuwenburgh, O.: Image-oriented distance parameterization for ensemble-based seismic history matching. Comput. Geosci. 21(4), 713–731 (2017)
Funding
Partial financial support was provided by the CIPR/IRIS cooperative research project “4-D Seismic History Matching,” which is funded by industry partners Eni, Petrobras, and Total EP NORGE, as well as the Research Council of Norway (PETROMAKS2).
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Mannseth, T., Fossum, K. Assimilating spatially dense data for subsurface applications—balancing information and degrees of freedom. Comput Geosci 22, 1323–1349 (2018). https://doi.org/10.1007/s10596-018-9755-3
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DOI: https://doi.org/10.1007/s10596-018-9755-3