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Abstract

Summary

The focus of this work is the introduction of a new Petrophysical Joint Inversion (PJI) to drastically improve reservoir characterization through a more robust petrophysical model that makes full use of the complementary information contained in multi-physics data such as seismic and Controlled Source ElectroMagnetics (CSEM). The advent of CSEM brings the possibility of integrating resistivity within the characterization workflow and reveals the potential to significantly improve the accuracy with which reservoir properties in general, and saturation in particular, can be determined. To reconcile the resistive anomalies at the reservoir scale, a 3D CSEM ‘localized’ model-based inversion is applied, capable of quantitatively reconstruct improved-resolution resistivities contained within the seismically derived vertical and lateral reservoir boundaries. Through a case study, based on a deep water oil field offshore Brazil, we demonstrate the power of PJI in the retrieval of reservoir parameters and to augment the certainty with which reservoir lithology and fluid properties are constrained.

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/content/papers/10.3997/2214-4609.201700848
2017-06-12
2024-04-20
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References

  1. Carcione, J.M., Ursin, B. and Nordskag, J.I.
    , 2007. Cross-property relations between electrical conductivity and the seismic velocity of rocks. Geophysics, 72, 193–204.
    [Google Scholar]
  2. Chen, J. and T. A.Dickens
    , 2009. Effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlled-source electromagnetics data. Geophysical Prospecting, 57, 61–74.
    [Google Scholar]
  3. Dell’AversanaP., BernasconiG., MiottiF. and RovettaD.
    , 2011. Joint inversion of rock properties from sonic, resistivity and density well-log measurements. Geophysical Prospecting, 59, 1144–1154.
    [Google Scholar]
  4. Hilterman, F.J.
    , 2001. Seismic Amplitude Interpretation. Distinguished Instructor Short Course, Distinguished Instructor Series, No. 4, SEG.
    [Google Scholar]
  5. Miotti, F., Guerra, I., Ceci, F., Lovatini, A., Paydayesh, M., Leathard, M., Sharma, A.
    , 2013. Petrophysical Joint Inversion of seismic and EM attributes: a case study. SEG Technical Program Expanded Abstract, 2516–2521.
    [Google Scholar]
  6. Mavko, G., Mukerji, T., Dvorkin, J.
    , 2009. The Rock Physics Handbook, Tools for Seismic Analysis of Porous Media. ISBN: 978052186136.
    [Google Scholar]
  7. Tarantola, A.
    , 2005. Inverse Problem Theory. SIAM. ISBN9780898715729.
    [Google Scholar]
  8. Zerilli, A., Buonora, M.P., Labruzzo, T.
    , 2011. Enhancing the resolution of mCSEM data using a hybrid-based inversion workflow. 73rd EAGE Conference & Exhibition, Vienna, Austria, Expanded Abstract, 23–26 May.
    [Google Scholar]
  9. Zerilli, A., Buonora, M.P., Menezes, P.T.L., Crepaldi, J.L.S. and Miotti, F.
    , 2016. Seismic - EM Integration -Building Confidence in Deep Water E&P. 78th EAGE Conference & Exhibition, Vienna, Austria, Expanded Abstracts, Z013.
    [Google Scholar]
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