Modeling Accurate P32 Values During DFN Building: No Longer Guessing Input Fracture Numbers

In this work we propose two analytical relationships which can be used to estimate the number of fractures (N) needed to produce a specific fracture intensity (P32) in a specific volume during DFN modeling. These relationships were obtained from a total of 30 numerical experiments in which models of varying volumes were populated with variable fracture numbers. The methodology was tested on 3 real datasets, which resulted in very close predictions of the desired P32 values, with discrepancies as low as −7%. This method requires as only inputs the volume to populate and the desired P32, and can help geologists who need to build DFN models to better estimate the number of fractures needed by the fracture-generating algorithms.