1887

Abstract

Summary

Tremendous advances have been made in the last two decades in joint inversion of multiple data sets. Coupling of all forms of geophysical data and flow data have been demonstrated. Advances in methods to link the data sets and associated parameters, both by structural and rock-physics coupling approaches have been key to the impressive results currently being demonstrated.

Looking forward it seems that applications of joint inversion to improve our reservoir flow models holds the largest benefit compared to other applications such as structural imaging. In the reservoir application, combining of both structural and rock-physics coupling in joint inversion of seismic, EM, gravity and flow data will undoubtedly be needed to maximize results.

Advances in theory and compute power will lead to stochastic MCMC based sampling techniques as the preferred method for joint inversion. MCMC techniques ability to provide a global solution, the easy of incorporating multiple and desperate a priori information, and accurate uncertainty estimation will all drive the field toward stochastic approaches.

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/content/papers/10.3997/2214-4609.201901984
2019-06-03
2024-03-28
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References

  1. Chen, J., Kemna, A., Hubbard, S.S.
    , 2008, A comparison between Gauss-Newton and Markov-chain Monte Carlo-based methods for inverting spectral induced-polarization data for Cole-Cole parameters., Geophysics, 73, F247–F259.
    [Google Scholar]
  2. Chen, J., Nihei, K., Hoversten, G.M., Bevc, D., and Stefani, J.
    , 2018. Stochastic Waveform Inversion (SWI) of Brand-limited Seismic Data for Long-wavelength Velocity Models. SEG18 Anaheim, CA. Workshop W-18, Friday Oct 19.
    [Google Scholar]
  3. Colombo, D., and Rovetta, D.
    , 2018. Coupling strategies in multiparameter geophysical joint inversion. Geoph. Jou. Int., 215, 1171–1184.
    [Google Scholar]
  4. Gallardo, L.A., and Meju, M.A.
    , 2003. Characterization of heterogeneous near-surface materials by joint 2D inversion of DC resistivity and seismic data. Geophys. Res. Lett., 30, 1658.
    [Google Scholar]
  5. Haber, E., and Oldenburg, D.W.
    , 1997. A general framework for constraint minimization for the inversion of electromagnetic measurements. Prog. Electromagn. Res., 46, 265–312.
    [Google Scholar]
  6. Hoversten, G.M., Royle, A., Chen, J., and Myer, D.
    , 2017. MCMC Inversion of Offshore West Africa AVA Data. B1 01, 79th EAGE Conference and Exhibition.
    [Google Scholar]
  7. Lelievre, P.G., Farquharson, C.G., and Hurich, C.A.
    , 2012. Joint inversion of seismic traveltimes and gravity data on unstructured grids with application to mineral exploration. Geophysics, 77, K1–K15
    [Google Scholar]
  8. Trainor-Guitton, W.J., Hoversten, G.M.
    , 2008, Stochastic inversions for electromagnetic geophysics: Practical challenges and improving convergence efficiency. Geophysics, 76, F373–F386.
    [Google Scholar]
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