MoFrac discrete fracture network (DFN) modeling software generates fracture network simulations with deterministic fractures constrained to known locations, and stochastic fractures conditioned to input data. A deterministic fracture network is generated through the modeling of a dataset that is representative of the lineaments typically found in a Canadian Shield environment. This model is used to constrain stochastic representations to observed fracture intensities and orientations. This study considers two- dimensional and three-dimensional length distributions and area distributions as constraints. Built-in metrics are used to analyze the size and orientation distributions of the stochastic models for comparison with the input data. Further calibration of constraints for these models is achieved by dividing fracture groups into subsets; this preprocessing task involves the definition of subsets of identified fracture groups based on orientation. The consistency and accuracy of the fracture network modeling are considered using three alternative conditioning methods. It was shown that generated fracture networks conform to the conditioning parameters for each method considered. Where multiple subsets were used to define fracture group parameters, resulting DFNs were more representative of the input data.

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