1887

Abstract

Summary

The problem of identifying large-scale subsurface structures utilizing information from both controlled source electromagnetic (CSEM) data and seismics is considered. The proposed inversion methodology can use interpreted seismic data as structural prior information in a Bayesian inversion of CSEM data. The Bayesian method applied is the ensemble Kalman filter, where the conductivity model is updated without requiring sensitivity calculations. The ensemble Kalman filter also provides the ability to quantify uncertainty in the conductivity model. To be able to represent complex subsurface conductivity distributions, we utilize the recently proposed hierarchical level-set (LS) parameterization, with reduced representation of the LS function. The novel inversion methodology is applied on three numerical examples: one where there is no reservoir present; another where two reservoirs are present; and a third where the prior model includes a reservoir which is not present in the reference model (‘false positive’). In all examples, the methodology is able to identify the large-scale background structures (strata boundaries and a fault) with reasonably good accuracy. Furthermore, the methodology accurately identifies existing reservoirs, and removes the reservoir in the case containing the ‘false positive’.

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/content/papers/10.3997/2214-4609.20141558
2014-06-16
2024-03-28
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References

  1. Berre, I., Lien, M. and Mannseth, T.
    [2011] Identification of three-dimensional electric conductivity changes from time-lapse electromagnetic observations. J. Comput. Phys., 230(10), 3915–3928.
    [Google Scholar]
  2. Cremers, D., Kohlberger, T. and Schnörr, C.
    [2003] Shape statistics in kernel space for variational image segmentation. Pattern Recogn., 36(9), 1929–1943.
    [Google Scholar]
  3. 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.
    [Google Scholar]
  4. Gallardo, L.A. and Meju, M.A.
    [2004] Joint two-dimensional DC resistivity and seismic travel time inversion with cross-gradients constraints. J. Geophys. Res., 109(B3), B03311.
    [Google Scholar]
  5. Lien, M.
    [2013] Simultaneous joint inversion of amplitude-versus-offset and controlled-source electromagnetic data by implicit representation of common parameter structure. Geophysics, 78(4), ID15–ID27.
    [Google Scholar]
  6. Mannseth, T.
    [2014] Relation between level set and truncated pluri-gaussian methodologies for facies representation. Math. Geosci., In press.
    [Google Scholar]
  7. Tveit, S., Bakr, S.A., Lien, M. and Mannseth, T.
    [2014] Identification of subsurface structures using electromagnetic data and shape priors. Submitted.
    [Google Scholar]
  8. Versteeg, R.
    [1994] The Marmousi experience: Velocity model determination on a synthetic complex data set. Lead. Edge, 13(9), 927–936.
    [Google Scholar]
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