1887

Abstract

Summary

We implement an AVA-petrophysical inversion that uses an anisotropic Markov random field to model the lateral variability of petrophysical properties. A Bayesian framework is adopted for transforming pre-stack data to the maximum-a-posteriori solution of reservoir properties. The lateral heterogeneity of the investigated reservoir is reasonably modeled by the Huber energy function. For computational feasibility reasons, we limit our attention to a target-oriented inversion that uses the amplitude versus angle (AVA) responses extracted along a time slice representing the top reflection of the investigated reservoir, to infer the petrophysical properties of interest for the reservoir layer. The implemented AVA-petrophysical inversion uses a previously defined linear rock-physics model to rewrite the linear Aki and Richards AVA equation for P-P waves in terms of contrasts in the petrophysical properties at the reflecting interface. This reformulation allows us to directly derive the petrophysical properties around the target zone from AVA data. We applied this method to 3D onshore seismic data for the characterization of a clastic, gas-saturated, reservoir. A comparison with the outcomes of a more standard laterally unconstrained AVA-petrophysical inversion is used to demonstrate the antinoise and the imaging ability of the implemented approach.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201800931
2018-06-11
2021-06-13
Loading full text...

Full text loading...

References

  1. Aki, K., and Richards, P. G.
    (1980). Quantative seismology: Theory and methods. New York, 801.
    [Google Scholar]
  2. Aleardi, M., Ciabarri, F., and Mazzotti, A.
    (2017a). Probabilistic estimation of reservoir properties by means of wide-angle AVA inversion and a petrophysical reformulation of the Zoeppritz equations. Journal of Applied Geophysics, 147, 28–41.
    [Google Scholar]
  3. Aleardi, M., Ciabarri, F., Gukov, T., Giussani, M., and Mazzotti, A.
    (2017b). A Single-step Bayesian Petrophysical Inversion Algorithm Based on a Petrophysical Reformulation of the P-wave Reflection Coefficients. In 79th EAGE Conference and Exhibition 2017. Doi: 10.3997/2214‑4609.201700546.
    https://doi.org/10.3997/2214-4609.201700546 [Google Scholar]
  4. Alemie, W., and Sacchi, M. D.
    , (2011). High-resolution three-term AVO inversion by means of a Trivariate Cauchy probability distribution. Geophysics, 76(3), R43–R55.
    [Google Scholar]
  5. Avseth, P., Mukerji, T., and Mavko, G.
    (2005). Quantitative seismic intrepretation. Cambridge university press.
    [Google Scholar]
  6. Charbonnier, P., Blanc-Feraud, L., Aubert, G., and Barlaud, M.
    , (1997). Deterministic edgepreserving regularization in computed imaging. IEEE Transactions on Image Processing, 6(2), 298–311.
    [Google Scholar]
  7. Chen, J. J., Yin, X. Y., and Zhang, G. Z.
    (2007). Simultaneous three-term AVO inversion based on Bayesian theorem. Journal of China University of Petroleum (Edition of Natural Science), 3, 006.
    [Google Scholar]
  8. Downton, J. E.
    , (2005). Seismic parameter estimation from AVO inversion. University of Calgary.
    [Google Scholar]
  9. Mazzotti, A.
    (1990). Prestack amplitude analysis methodology and application to seismic bright spots in the Po Valley, Italy. Geophysics, 55(2), 157–166.
    [Google Scholar]
  10. Rimstad, K., and Omre, H.
    , (2010). Impact of rock-physics depth trends and Markov random fields on hierarchical Bayesian lithology/fluid prediction. Geophysics, 75(4), R93–R108.
    [Google Scholar]
  11. Theune, U., Jensås, I. Ø., and Eidsvik, J.
    , (2010). Analysis of prior models for a blocky inversion of seismic AVA data. Geophysics, 75(3), C25–C35.
    [Google Scholar]
  12. Ulvmoen, M., Omre, H. and Buland, A.
    , (2010). Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations: Part 2-Real case study. Geophysics, 75(2), B73–B82.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201800931
Loading
/content/papers/10.3997/2214-4609.201800931
Loading

Data & Media loading...

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