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Impact of time-lapse seismic data for permeability estimation

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Abstract

We consider the impact of using time-lapse seismic data in addition to production data for permeability estimation in a porous medium with multiphase fluid flows, such as a petroleum reservoir under water-assisted production. Since modeling seismic wave propagation in addition to modeling fluid flows in the reservoir is quite involved, it is assumed that the time-lapse seismic data have already been inverted into fluid saturation differences (pseudoseismic data). Because an inversion process often leads to considerable error growth, we will consider pseudoseismic data with large uncertainties. The impact of pseudoseismic data is assessed through permeability estimation with and without such data and through application of some uncertainty measures for the estimated parameters. A multiscale algorithm is used for the parameter estimations, so that potential differences in attainable permeability resolution will be easily revealed. The numerical examples clearly indicate that the permeability estimation problem is stabilized at a higher level of resolution when pseudoseismic data are applied in addition to production data, even if the pseudoseismic data have large associated uncertainties. Use of the parameter uncertainty measures confirm these results.

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References

  1. Aanonsen, S.I.: Efficient history matching using a multiscale technique. SPE Reserv. Evalu. Eng. 11(1), 154–164 (2008)

    Google Scholar 

  2. Aanonsen, S.I., Aavatsmark, I., Barkve, T., Cominelli, A., Gonard, R., Gosselin, O., Kolasinski, M., Reme, H.: Effect of scale dependent data correlations in an integrated history matching loop combining production data and 4D seismic data. In: Proc. SPE Reservoir Simulation Symposium, Paper SPE 79665. Houston, Texas (2003)

  3. Anterion, F., Karcher, B., Eymard, R.: Use of parameter gradients for reservoir history matching. In: Proc. 10th SPE Reservoir Simulation Symposium, Paper SPE 18433 (1989)

  4. Arenas, E., van Kruijsdijk, C., Oldenziel, T.: Semi-automatic history matching using the pilot point method including time-lapse seismic data. In: 2001 SPE Annual Technical Conference and Exhibition, Paper SPE 71634. New Orleans (2001)

  5. Aziz, K., Settari, A.: Petroleum Reservoir Simulation. Applied Science, London (1979)

  6. Behrens, R., Condon, P., Haworth, W., Bergeron, M., Wang, Z., Ecker, C.: 4D seismic monitoring of water flux at Bay Marchand: the practical use of 4D in an imperfect world. SPE Reserv. Evalu. Eng. 5(5), 410–420 (2002)

    Google Scholar 

  7. Ben Ameur, H., Chavent, G., Jaffré, J.: Refinement and coarsening indicators for adaptive parameterization: application to the estimation of hydraulic transmissivities. Inverse Probl. 18, 775–794 (2002)

    Article  MATH  Google Scholar 

  8. Chavent, G., Liu, J.: Multiscale parameterization for the estimation of a diffusion coefficient in elliptic and parabolic problems. In: Proc. Fifth IFAC Symposium on Control of Distributed Parameter Systems, pp. 193–202. Perpignan, France (1989)

  9. Dadashpour, M., Landrø, M., Kleppe, J.: Nonlinear inversion for estimating reservoir parameters from time-lapse seismic data. J. Geophys. Eng. 5, 54–66 (2008)

    Article  Google Scholar 

  10. Dong, Y., Oliver, D.S.: Quantitative use of 4D seismic data for reservoir description. SPE J. 10(1), 91–99 (2005)

    Google Scholar 

  11. Doyen, P.M.: Seismic Reservoir Characterization: An Earth Modelling Perspective. EAGE, Houten (2007)

  12. Feng, T., Edström, P., Gulliksson, M.: Levenberg–Marquardt methods for parameter estimation problems in the radiative transfer equation. Inverse Probl. 23(3), 879–891 (2007)

    Article  MATH  Google Scholar 

  13. Feng, T., Mannseth, T.: Improvements on a predictor–corrector strategy for parameter estimation with several data types. Inverse Probl. 25(10) (2009)

  14. Feng, T., Mannseth, T., Aanonsen, S.I.: Randomized maximum likelihood with permeability samples generated by a predictor corrector technique. In: Proceeding of the 2009 SPE Reservoir Simulation Symposium, Paper SPE 118975. The Woodlands, Texas, USA (2009)

  15. Gosselin, O., Aanonsen, S.I., Aavatsmark, I., Cominelli, A., Gonard, R., Kolasinski, M., Ferdinandi, F., Kovacic, L., Neylon, K.: History matching using time-lapse seismic (HUTS). In: Proc. SPE Annual Technical Conference and Exhibition, Paper SPE 84464. Denver, Colorado, USA (2003)

  16. Greaves, R.J., Fulp, T.J.: Three-dimensional seismic monitoring of an enhanced oil recovery process. Geophysics 52(9), 1175–1187 (1987)

    Article  Google Scholar 

  17. Grimstad, A.A., Mannseth, T.: Comparison of methods for downscaling of coarse scale permeability estimates. In: Proc. 9th European Conference on Mathematics of Oil Recovery. Cannes, France (2004)

  18. Grimstad, A.A., Mannseth, T., Aanonsen, S., Aavatsmark, I., Cominelli, A., Mantica, S.: Identification of unknown permeability trends from history matching of production data. SPE J. 9(4), 419–428 (2004)

    Google Scholar 

  19. Grimstad, A.A., Mannseth, T., Nævdal, G., Urkedal, H.: Adaptive multiscale permeability estimation. Comput. Geosci. 7(1), 1–25 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  20. Huang, X., Meister, L., Workman, R.: Reservoir characterization by integration of time-lapse seismic and production data. In: 1997 SPE Annual Technical Conference and Exhibition, Paper SPE 38695. San Antonio, Texas (1997)

  21. Kretz, V., le Ravalec-Dupin, M., Roggero, F.: An integrated reservoir characterization study matching production data and 4D seismic. SPE Reserv. Evalu. Eng. 7(2), 116–122 (2004)

    Google Scholar 

  22. Krüger, H., Mannseth, T.: Extension of the parameterization choices in adaptive multiscale estimation. Inverse Probl. Sci. Eng. 13(5), 469–484 (2005)

    Article  Google Scholar 

  23. Landa, J.L., Horne, R.N.: A procedure to integrate well test data, reservoir performance history and 4-D seismic information into a reservoir description. In: Proc. 1997 SPE Annual Technical Conference and Exhibition, Paper SPE 38653. San Antonio, Texas (1997)

  24. Landrø, M.: Discrimination between pressure and fluid saturation changes from time-lapse seismic data. Geophysics 66, 836–844 (2001)

    Article  Google Scholar 

  25. Liu, J.: A multiresolution method for distributed parameter estimation. SIAM J. Sci. Comput. 14(2), 389–405 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  26. Lu, P., Horne, R.N.: Multiresolution approach to reservoir parameter estimation using wavelet analysis. In: Proc. 2000 SPE Annual Technical Conference and Exhibition, Paper SPE 62985. Dallas, TX (2000)

  27. Mannseth, T.: Permeability identification from pressure observations: some foundations for multiscale regularization. Multiscale Model. Simul. 5(1), 21–44 (electronic) (2006)

    Article  MATH  MathSciNet  Google Scholar 

  28. Moré, J.: The Levenberg–Marquardt Algorithm: Implementation and Theory, Chap. 630, pp. 105–116. Lecture Notes in Mathematics. Springer, Berlin (1978)

    Google Scholar 

  29. Nielsen, L.K., Li, H., Tai, X.C., Aanonsen, S.I., Espedal, M.: Reservoir description using a binary level set model (2009). Comput. Vis. Sci. 13, 41–58

    Article  MathSciNet  Google Scholar 

  30. Sahni, I., Horne, R.N.: Generating multiple history-matched reservoir-model realizations using wavelets. SPE Reserv. Evalu. Eng. 9(3), 217–226 (2006)

    Google Scholar 

  31. Schlumberger GeoQuest: ECLIPSE 100 Reference Manual Version 2004a (2004)

  32. Sen, A., Srivastava, M.: Regression Analysis: Theory, Methods and Applications. Springer, New York (1990)

    MATH  Google Scholar 

  33. Stephen, K.D., MacBeth, C.: Reducing reservoir prediction uncertainty by updating a stochastic model using seismic history matching. SPE Reserv. Evalu. Eng. 11(6), 991–999 (2008)

    Google Scholar 

  34. Vasco, D.W., Datta-Gupta, A., Behrens, R., Condon, P., Rickett, J.: Seismic imaging of reservoir flow properties: time lapse amplitude changes. Geophysics 69(6), 1425–1442 (2004)

    Article  Google Scholar 

  35. Waggoner, J.R., Cominelli, A., Seymour, R.H.: Improved reservoir modeling with time-lapse seismic in a Gulf of Mexico gas condensate reservoir. In: Proc. SPE Annual Technical Conference and Exhibition, Paper SPE 77514. San Antonio, Texas (2002)

  36. Wayland, J.R., Lee, D.: Seismic mapping of EOR processes. Lead. Edge 5(12), 36–40 (1986)

    Article  Google Scholar 

  37. Yoon, S., Malallah, A.H., Datta-Gupta, A., Vasco, D.W., Behrens, R.A.: A multiscale approach to production data integration using streamline methods. SPE J. 6(2), 182–192 (2001)

    Google Scholar 

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Feng, T., Mannseth, T. Impact of time-lapse seismic data for permeability estimation. Comput Geosci 14, 705–719 (2010). https://doi.org/10.1007/s10596-010-9182-6

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