Intégration de connaissances sédimentologiques et stratigraphiques dans la modélisation 3D des faciès sédimentaires marins

Institut National Polytechnique de Lorraine

Abstract

An original method for the generation of 3D facies model has been implemented. It simultaneously accounts for well and seismic data, sedimentological rules describing the spatial distribution of rock types, or sequence stratigraphy principles. Different probability cubes are computed by integrating one or several pieces of information controlling the facies occurrence: the relationship binding the facies to the paleolandscape, the lateral transitions of facies, the stratigraphic control of shoreline migration, the sediment volume partitioning or the diagenesis potential. The generated probability cubes can then be combined, considering the redundancy of the data they express. The suggested methodology provides therefore an extensible framework for the integration and the combination of data from diverse origins and types, sometimes redundant and whose weight in the final model can be balanced according to the data uncertainty.

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BibTeX Reference

@PHDTHESIS{Kedzierski2007a,
    author = { Kedzierski, Pierre },
     title = { Intégration de connaissances sédimentologiques et stratigraphiques dans la modélisation 3D des faciès sédimentaires marins },
      year = { 2007 },
    school = { Institut National Polytechnique de Lorraine },
  abstract = { An original method for the generation of 3D facies model has been implemented. It simultaneously accounts for well and seismic data, sedimentological rules describing the spatial distribution of rock types, or sequence stratigraphy principles. Different probability cubes are computed by integrating one or several pieces of information controlling the facies occurrence: the relationship binding the facies to the paleolandscape, the lateral transitions of facies, the stratigraphic control of shoreline migration, the sediment volume partitioning or the diagenesis potential. The generated probability cubes can then be combined, considering the redundancy of the data they express. The suggested methodology provides therefore an extensible framework for the integration and the combination of data from diverse origins and types, sometimes redundant and whose weight in the final model can be balanced according to the data uncertainty. }
}