In iterative geostatistical seismic inversion methods, the existing experimental data (i.e., the available well-log data) is considered hard-data without any uncertainty. However, some regions of the well may have different levels of reliability, due to, for instance, problems during well-log acquisition. In these cases, the well-log measurements should not be reproduced in the inverted elastic models. Instead, the intrinsic uncertainty associated with each measurement should be included within the inversion procedure since it may carry secondary information that might be useful in guiding the inference of the petro-elastic properties of interest. In this work, we propose a geostatistical framework to account consistently and throughout the entire iterative inversion procedure different layers of uncertainty. In this way, the global stochastic inversion algorithm was adapted to include stochastic sequential simulation with local probability distribution functions as the model perturbation technique to handle local uncertainties along the well path. The proposed methodology was applied to a real case study where we tackled the uncertainty assessment of well-log data and its propagation to the simulation process using stochastic sequential simulation algorithm to incorporate local probability distribution functions associated to the possible acquisition measurement errors due to collapsed zones in the borehole walls.


Article metrics loading...

Loading full text...

Full text loading...


  1. Azevedo, L., Nunes, R., Correia, P., Soares, A., Guerreiro, L. and Neto, G. S.
    [2014] Multidimensional scaling for the evaluation of a geostatistical seismic elastic inversion methodology. Geophysics, 79(1), M1–M10.
    [Google Scholar]
  2. Azevedo, L., and Soares, A.
    [2017] Geostatistical Methods for Reservoir Geophysics: Springer International Publishing, 1st Edition, Advances in Oil and Gas Exploration & Production.
    [Google Scholar]
  3. Figueiredo, L. P., Santos, M., Roisenberg, M., Neto, G. and Figueiredo, W.
    [2014] Bayesian framework to wavelet estimation and linearized acoustic inversion: Geoscience and Remote Sensing Letters, IEEE, 11, 2130–2134.
    [Google Scholar]
  4. Nunes, R., Soares, A., Schwedersky, G., Dillon, L., Guerreiro, L., Caetano, H. and Leon, F.
    [2012] Geostatistical inversion of pre-stack seismic data: Ninth International Geostatistics Congress, Springer, 1–8.
    [Google Scholar]
  5. Soares, A.
    [2001] Direct sequential simulation and co-simulation: Mathematical Geology, 33, 911–926.
    [Google Scholar]
  6. Soares, A., Diet, J. and Guerreiro, L.
    [2007] Stochastic inversion with a global perturbation method: Presented at the Petroleum Geostatistics, 2007, EAGE.
    [Google Scholar]
  7. Soares, A., Nunes, R. and Azevedo, L.
    [2017] Integration of Uncertainty Data in Geostatistical Modelling: Mathematical Geosciences, 49, issue 2, pp 253–273.
    [Google Scholar]
  8. Tarantola, A.
    [2005] Inverse problem theory and methods for model parameter estimation: Society for Industrial and Applied Mathematics.
    [Google Scholar]
  9. Tompkins, M. J., Fernández Martínez, J. L., Alumbaugh, D. L. and Mukerji, T.
    [2011] Scalable uncertainty estimation for nonlinear inverse problems using parameter reduction, constraint mapping, and geometric sampling: Marine controlled-source electromagnetic examples: Geophysics, 76, F263–F281.
    [Google Scholar]

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