Sampling uncertainty about segmented normal fault interpretation using a stochastic downscaling method.

@inproceedings{JulioGM2014,
 abstract = { A large-scale normal fault may be seen as composed of several overlapping fault segments separated by relay zones at a finer scale. In this paper, we propose an automatic and stochastic method to sub-divide (downscale) a segmented normal fault into en-echelon segments that may be linked by connecting faults. The downscaling algorithm is composed of three main steps. (1) The first step consists in detecting the segments using geometrical criteria. (2) Then the overlapping segments are modeled using isolated fault descriptions and statistics. (3) Lastly, the maturity of the simulated relay zones is evaluated based on relay geometry. If a given maturity threshold is reached, the relay ramp is breached. These three downscaling steps depend on seven parameters that can be defined constant or randomly chosen from probability distributions to sample uncertainties. The method has been applied to a large normal fault laterally limiting a hydrocarbon reservoir and poorly imaged from seismic data. A Monte Carlo sampling is used to handle uncertainty related to the structure geometry. The stochastic downscaling simulation results in modeling fault arrays with different geometries, which vary in segment number, in relative segment position and in linkage maturity. We also show on an example the significant impact of fault segmentation uncertainty on dynamic reservoir behavior. },
 author = { Julio, Charline AND Caumon, Guillaume AND Ford, Mary },
 booktitle = { Proc. 34th Gocad Meeting, Nancy },
 title = { Sampling uncertainty about segmented normal fault interpretation using a stochastic downscaling method. },
 year = { 2014 }
}