In naturally fractured reservoirs characterization and modelling, a key fracture parameter is fracture density. Classically, fracture density along well trajectories is estimated from image log data interpretation using various methods and applying corrections to account for 1D sampling bias. Various methods and concepts are then used to extrapolate/interpolate the fracture density beyond the well control. The well density data are often assumed to be hard data (precisely known). It has been showed, however, that this should not be the case. Consequently, estimating fracture density uncertainty is a key issue in fracture modelling.

Methods have been proposed to estimate fracture density and related uncertainty but they are based on assumptions (fractures nearly parallel to the borehole, randomly distributed fractures) that are always met. The fracture density can also be controlled by many different geological constraints: fracture clustering, bed thickness, stress shadow effect...

This paper presents a methodology to simulate DFN that can be controlled by various types of geological constraints in order to assess the uncertainty attached to wells drilled through the simulated DFN. Uncertainty tables are generated, which can then be applied to well fracture data in real NFR reservoirs.


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