Intégration multi-échelles des données de réservoir et quantification des incertitudes

Université de Lorraine

Abstract

In this work, we propose to follow a multiscale approach for spatial reservoir properties characterization using direct (well observations) and indirect (seismic and production history) data at different resolutions. Two decompositions are used to parameterize the problem : the wavelets and the Gaussian pyramids. Using these parameterizations, we show the advantages of the multiscale approach with two uncertainty quantification problems based on minimization. The first one concerns the simulation of property fields from a multiple points geostatistics algo- rithm. It is shown that the multiscale approach based on Gaussian pyramids improves the quality of the output realizations, the match of the conditioning data and the computational time compared to the standard approach. The second problem concerns the preservation of the prior models during the assimilation of the production history. In order to re-parameterize the problem, we develop a new 3D grid adaptive wavelet transform, which can be used on complex reservoir grids containing dead or zero volume cells. An ensemble-based optimization method is integrated in the multiscale history matching approach, so that an estimation of the uncertainty is obtained at the end of the optimization. This method is applied on several application examples where we observe that the final realizations better preserve the spatial distribution of the prior models and are less noisy than the realizations updated using a standard approach, while matching the production data equally well.

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BibTeX Reference

@PHDTHESIS{,
    author = { Gentilhomme, Théophile },
     title = { Intégration multi-échelles des données de réservoir et quantification des incertitudes },
      year = { 2014 },
    school = { Université de Lorraine },
       url = { http://ring.gocad.org/ring_dl/public/publications/2014PhdGentilhomme.pdf },
  abstract = { In this work, we propose to follow a multiscale approach for spatial reservoir properties characterization using direct (well observations) and indirect (seismic and production history) data at different resolutions. Two decompositions are used to parameterize the problem : the wavelets and the Gaussian
pyramids. Using these parameterizations, we show the advantages of the multiscale approach with two
uncertainty quantification problems based on minimization.
The first one concerns the simulation of property fields from a multiple points geostatistics algo-
rithm. It is shown that the multiscale approach based on Gaussian pyramids improves the quality of
the output realizations, the match of the conditioning data and the computational time compared to
the standard approach.
The second problem concerns the preservation of the prior models during the assimilation of the
production history. In order to re-parameterize the problem, we develop a new 3D grid adaptive wavelet
transform, which can be used on complex reservoir grids containing dead or zero volume cells. An
ensemble-based optimization method is integrated in the multiscale history matching approach, so that
an estimation of the uncertainty is obtained at the end of the optimization. This method is applied
on several application examples where we observe that the final realizations better preserve the spatial
distribution of the prior models and are less noisy than the realizations updated using a standard
approach, while matching the production data equally well. }
}