Natural fracture networks (NFNs) are used in unconventional reservoir simulators to model pressure and saturation changes in fractured rocks. These fracture networks are often derived from well data or well data combined with a variety of seismic-derived attributes to provide spatial information away from the wells. In cases in which there is a correlation between faults and fractures, the use of a fault indicator can provide additional constraints on the spatial location of the natural fractures. We use a fault attribute based on fault-oriented semblance as a secondary conditioner for the generation of NFNs. In addition, the distribution of automatically extracted faults from the fault-oriented semblance is used to augment the well-derived statistics for natural fracture generation. Without the benefit of this automated fault-extraction solution, to manually extract the fault-statistical information from the seismic data would be prohibitively tedious and time consuming. Finally, we determine, on a 3D field unconventional data set, that the use of fault-oriented semblance results in simulations that are significantly more geologically reasonable.