Conditioning of the 3D stochastic modeling of fault networks

Charline Julio. ( 2015 )
Université de Lorraine

Abstract

Faults are discontinuities in rock volumes that affect mechanical properties and flow paths of hydrocarbon reservoirs. However, subsurface modeling remains limited by the incompleteness and resolution of available data, so that uncertainties remain on the geometry and the connectivity of fault networks. To assess fault network uncertainties, several stochastic approaches have been introduced in the literature. These methods generate a set of possible fault models conditioned by reservoir data. In this thesis, we investigate two main conditioning strategies of stochastic fault modeling methods. The first one takes into account the observations of the fault absence, for instance, as indicated by seismic reflector continuity. To do this, the reservoir volume is divided into two sub-volumes delimited by a 3D envelope surface: (1) a volume where no faults occur, and (2) a potentially-faulted volume. Then, faults are simulated and optimized in such a way as to be entirely confined to the potentially-faulted volume. The second presented strategy deals with the uncertainties related to the seismic interpretation of fault segmentation. It generates a set of fine-scale segmented faults from a larger-scale and continuous interpretation of the fault. The method uses the orientation variations of the continuous fault to subdivide it into several possible fault segments. The effects of the different segmentation configurations on flow simulations are studied.

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    BibTeX Reference

    @PHDTHESIS{,
        author = { Julio, Charline },
         title = { Conditioning of the 3D stochastic modeling of fault networks },
          year = { 2015 },
        school = { Université de Lorraine },
      abstract = { Faults are discontinuities in rock volumes that affect mechanical properties and flow paths of hydrocarbon reservoirs. However, subsurface modeling remains limited by the incompleteness and resolution of available data, so that uncertainties remain on the geometry and the connectivity of fault networks. To assess fault network uncertainties, several stochastic approaches have been introduced in the literature. These methods generate a set of possible fault models conditioned by reservoir data. 
    	In this thesis, we investigate two main conditioning strategies of stochastic fault modeling methods. The first one takes into account the observations of the fault absence, for instance, as indicated by seismic reflector continuity. To do this, the reservoir volume is divided into two sub-volumes delimited by a 3D envelope surface: (1) a volume where no faults occur, and (2) a potentially-faulted volume. Then, faults are simulated and optimized in such a way as to be entirely confined to the potentially-faulted volume. The second presented strategy deals with the uncertainties related to the seismic interpretation of fault segmentation. It generates a set of fine-scale segmented faults from a larger-scale and continuous interpretation of the fault. The method uses the orientation variations of the continuous fault to subdivide it into several possible fault segments. The effects of the different segmentation configurations on flow simulations are studied. }
    }