1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. IOR 2017 - 19th European Symposium on Improved Oil Recovery
  5. Conference paper

Abstract

Summary

A distance parameterization of flood fronts derived from time-lapse seismic anomalies was recently developed to facilitate incorporation of time-lapse seismic data into history matching workflows based on ensemble methods such as the ensemble Kalman filter (EnKF) and the ensemble smoother (ES). A number of advantages were demonstrated on synthetic data including a significant reduction in the number of data points and flexibility in the type of attribute from which the front information can be extracted. In order to enable the use of the proposed method in real-field history-matching cases, we first extended the applicability of the algorithm computing the distance between observed and simulated fronts from regular Cartesian grids to generic corner-pint grids. Secondly, we used concepts from image analysis to generalize the innovations from the distance parameterization used in the EnKF as a directed local Hausdorff distance (from simulated to observed fronts) whereby a further improvement was achieved by taking into account the reverse measure (from observed to simulated fronts) as well. The workflow was subsequently applied to a series of numerical experiments on synthetic realistically complex test cases with promising results. The next step is a comprehensive examination on real field data as an objective of the study supported by National IOR Centre Norway. In this paper, we apply the history matching workflow to the Norne field where multiple high-quality seismic surveys were conducted. An iterative ES is used for history matching. The estimated model parameters include permeability, porosity, net-to-gross ratio, vertical transmissibility multipliers, fault transmissibility multipliers and saturation endpoints of relative permeability curves. The observations of front positions are acquired from an inversion of the Norne AVO seismic data set. Special attention is paid to the generation of the initial ensemble of reservoir models and the interpretation of inverted seismic data to ensure a proper estimation of the uncertainties for both model variables and data. The results show that additional benefits are received by matching to both production and 4D seismic data which contributes a better understanding of the reservoir and some new insights are gained regarding the performance of the proposed method. The outcomes of the application to the Norne field cases also suggest a couple of topics that are worth of further investigation. The order in which production and seismic data are incorporated, the localization approach, and for example parameterization of production data could all potentially improve the results.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201700363
2017-04-24
2024-04-19

  • From This Site

    /content/papers/10.3997/2214-4609.201700363
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aanonsen, S.I., Nævdal, G., Oliver, D.S., Reynolds, A.C. and Vallès, B.
    [2009] Ensemble Kalman Filter in Reservoir Engineering — a Review. SPE Journal, 14(3), 393–412.
     [Google Scholar]
  2. Abadpour, A., Bergey, P. and Piasecki, R.
    [2013] 4D Seismic History Matching With Ensemble Kalman Filter-Assimilation on Hausdorff Distance to Saturation Front. In: SPE Reservoir Simulation Symposium. Society of Petroleum Engineers.
     [Google Scholar]
  3. Aronsen, H.A., Osdal, B., Dahl, T., Eiken, O., Goto, R., Khazanehdari, J., Pickering, S. and Smith, P.
    [2004] Time will tell: New insights from time-lapse seismic data. Oilfield Review, 16(2), 6–15.
     [Google Scholar]
  4. Baudrier, É., Nicolier, F., Millon, G. and Ruan, S.
    [2008] Binary-image comparison with local-dissimilarity quantification. Pattern Recognition, 41(5), 1461–1478.
     [Google Scholar]
  5. Chen, Y. and Oliver, D.S.
    [2010] Cross-covariances and localization for EnKF in multiphase flow data assimilation. Comput. Geosci., 14, 579–601.
     [Google Scholar]
  6. [2013] Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Computational Geosciences, 17(4), 689–703.
     [Google Scholar]
  7. [2014] History matching of the Norne full-field model with an iterative ensemble smoother. SPE Reservoir Evaluation & Engineering, 17(02), 244–256.
     [Google Scholar]
  8. Davies, D., Campbell, S., Ibram, M., Milne, R. and Mathieson, A.
    [2015] 100 4D Surveys: Adventures and Misadventures. In: SPE Offshore Europe Conference and Exhibition. Society of Petroleum Engineers.
     [Google Scholar]
  9. Dovera, L. and Della Rossa, E.
    [2011] Multimodal ensemble Kalman filtering using Gaussian mixture models. Computational Geosciences, 15, 307–323.
     [Google Scholar]
  10. Emerick, A. and Reynolds, A.
    [2011] Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications. Computational Geosciences, 15(2), 251–269.
     [Google Scholar]
  11. Emerick, A.A. and Reynolds, A.C.
    [2013] Ensemble smoother with multiple data assimilation. Computers & Geosciences, 55(0), 3 – 15.
     [Google Scholar]
  12. Gu, Y. and Oliver, D.S.
    [2007] An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation. SPE Journal, 12(4), 438–446.
     [Google Scholar]
  13. Hamming, R.W.
    [1950] Error detecting and error correcting codes. Bell System technical journal, 29(2), 147–160.
     [Google Scholar]
  14. Hassouna, M. and Farag, A.
    [2007] MultiStencils Fast Marching Methods: A Highly Accurate Solution to the Eikonal Equation on Cartesian Domains. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(9), 1563–1574.
     [Google Scholar]
  15. Hoteit, I., Luo, X. and Pham, D.T.
    [2012] Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters. Monthly Weather Review, 140(2), 528–542.
     [Google Scholar]
  16. Iglesias, M.A. and Dawson, C.
    [2013] The regularizing Levenberg-Marquardt scheme for history matching of petroleum reservoirs. Computational Geosciences, 17(6), 1033–1053.
     [Google Scholar]
  17. Jafarpour, B. and Khodabakhshi, M.
    [2011] A Probability Conditioning Method (PCM) for Nonlinear Flow Data Integration into Multipoint Statistical Facies Simulation. Math. Geosci., 43, 133–164.
     [Google Scholar]
  18. Jafarpour, B. and McLaughlin, D.B.
    [2008] History matching with an ensemble Kalman filter and discrete cosine parameterization. Comput. Geosci., 12(2), 227–244.
     [Google Scholar]
  19. Leeuwenburgh, O. and Arts, R.
    [2014] Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter. Computational Geosciences, 18(3–4), 535–548.
     [Google Scholar]
  20. Leeuwenburgh, O., Meekes, S., Vandeweijer, V. and Brouwer, J.
    [2016] Stochastic history matching to time-lapse seismic of a CO2-EOR project sector model. International Journal of Greenhouse Gas Control.
     [Google Scholar]
  21. Liu, N. and Oliver, D.S.
    [2004] Automatic History Matching of Geologic Facies. SPE Journal, 9(4), 188–195.
     [Google Scholar]
  22. Lorentzen, R.J., Nævdal, G. and Shafieirad, A.
    [2012] Estimating Facies Fields by Use of the Ensemble Kalman Filter and Distance Functions–Applied to Shallow-Marine Environments. SPE Journal, 3(1), 146–158.
     [Google Scholar]
  23. Luo, X., Stordal, A.S., Lorentzen, R.J. and Nævdal, G.
    [2015] Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem: Theory and Applications. SPE Journal, 20(05), 962–982.
     [Google Scholar]
  24. Mikkelsen, P.L., Guderian, K. and du Plessis, G.
    [2008] Improved reservoir management through integration of 4D-seismic interpretation, Draugen field, Norway. SPE Reservoir Evaluation & Engineering, 11(01), 9–17.
     [Google Scholar]
  25. Obidegwu, D., Chassagne, R. and MacBeth, C.
    [2015] Seismic Assisted History Matching Using Binary Image Matching. In: EUROPEC 2015. Society of Petroleum Engineers.
     [Google Scholar]
  26. Oliver, D.S. and Chen, Y.
    [2011] Recent Progress on Reservoir History Matching: a Review. Comput. Geosci., 15(1), 185–221.
     [Google Scholar]
  27. Osdal, B., Husby, O., Aronsen, H.A., Chen, N. and Alsos, T.
    [2006] Mapping the fluid front and pressure buildup using 4D data on Norne Field. The Leading Edge, 25(9), 1134–1141.
     [Google Scholar]
  28. Osher, S. and Sethian, J.A.
    [1988] Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1), 12 – 49.
     [Google Scholar]
  29. Safar, M.H. and Shahabi, C.
    [2003] Image Similarity Measures. Springer US, Boston, MA, 9–11.
     [Google Scholar]
  30. Sarma, P. and Chen, W.
    [2009] Generalization of the ensemble Kalman filter using kernels for non-gaussian random fields, SPE-119177. SPE Reservoir Simulation Symposium, 2–4 February, The Woodlands, Texas, USA.
     [Google Scholar]
  31. Tillier, E., Veiga, S.D. and Derfoul, R.
    [2013] Appropriate formulation of the objective function for the history matching of seismic attributes. Computers & Geosciences, 51(0), 64 – 73.
     [Google Scholar]
  32. Trani, M., Arts, R. and Leeuwenburgh, O.
    [2012] Seismic history matching of fluid fronts using the ensemble Kalman filter. SPE Journal, 18(01), 159–171.
     [Google Scholar]
  33. Veltkamp, R.C. and Hagedoorn, M.
    [2001] State of the Art in Shape Matching. SpringerLondon, 87–119.
     [Google Scholar]
  34. Zhang, Y. and Leeuwenburgh, O.
    [2016] Ensemble-based Seismic History Matching with Distance Parameterization for Complex Grids. In: ECMOR XIV-15th European Conference on the Mathematics of Oil Recovery.
     [Google Scholar]
  35. Zhang, Y. and Oliver, D.S.
    [2011] Evaluation and Error Analysis: Kalman Gain Regularization versus Covariance Regularization. Comput. Geosci., 15(3), 489–508.
     [Google Scholar]
  36. Zhang, Y., Oliver, D.S., Chauris, H. and Donno, D.
    [2015] Ensemble-based data assimilation with curvelets regularization. Journal of Petroleum Science and Engineering, 136, 55 – 67.
     [Google Scholar]
  37. Zhang, Y., Oliver, D.S., Chen, Y. and Skaug, H.J.
    [2014] Data Assimilation by Use of the Iterative Ensemble Smoother for 2D Facies Models. SPE Journal, 20(01), 169–185.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201700363
Loading
4D Seismic History Matching of the Norne Field Model Using Ensemble-based Methods with Distance Parameterization
Conference Proceedings 2017, 1 (2017); https://doi.org/10.3997/2214-4609.201700363
/content/papers/10.3997/2214-4609.201700363
/content/papers/10.3997/2214-4609.201700363
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error