Microseismic event analysis is a valuable source of information that can play a pivotal role in optimizing well completion and spacing. This analysis can be taken a step further with the generation of discrete fracture networks (DFNs) from microseismic events. While DFNs can be modeled with microseismic event locations only, source mechanisms inverted from near surface-acquired microseismic data provide greater constraints for the DFN model so that the orientation of failure planes responsible for events can be explicitly assigned. The differences between such DFN realizations based on event locations only and source-mechanism constrained DFN realizations are evident in areas with significant geological complexity.

Three iterations of a DFN model were produced from a microsesimic monitoring project in the Barnett shale. The fracture network of the first iteration is modeled stochastically using only basic geologic assumptions for the area and microseismic event locations and the orientations of trends formed by the events. The second iteration is refined by deterministically locating fractures in the model and defining the fracture orientations using a source mechanism determined from the microseismic point set. The third iteration uses the results from a mechanism scan on an event per event basis to determine the best source mechanism that fits the polarity reversal signature observed on the surface array.

Refining the model by determining the mechanism of individual events can identify multiple fracture orientations within the point set. In this data set two distinct mechanisms were identified, further analysis of which identified separate event energy distributions for the two mechanisms.

The changes in the model can be quantitatively evaluated with analysis of flow properties generated from the DFN and output to the stimulated reservoir volume (SRV). While changes in the SRV and total fracture volume for models presented in this study are most significant between the first two iterations, the total permeability change across the geocellular volume is significant between all three iterations.

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