In the Horn River Basin, evidence points to one of the key elements of a successful well completion being the effective connection of the hydraulic fracture into the natural fracture system, allowing the well to connect to a significantly enhanced drainage volume. However great uncertainty exists with respect to the nature of the natural fracture system and importantly how the hydraulic fracture interacts with it.

To provide a framework to address these uncertainties, an approach has been developed within a Discrete Fracture Network (DFN) code. The DFN approach provides a platform to construct realistic fracture models of stochastically generated fracture elements constrained and conditioned by well and surface data. Simulation of hydraulic frac development through these DFN models using a rule based approach allows the rapid modelling of the interaction between the hydraulic frac and the natural fracture system with calibration of the model being provided by the generation of a simulated micro-seismic cloud. Comparison of the simulated micro-seismic pattern to field measurements increases confidence in the DFN approach and allows the tuning of key hydraulic properties as part of the calibration process.

An additional challenge at this stage of the plays development however is that many of the developments are relatively data poor and therefore the rigorous simulation of detailed models conditioned to well data is often not possible. To address this, a number of simulations were run on more generic models where key properties such as fracture length, fracture aperture and intensity were varied and their impact on the resultant micro-seismic pattern observed. This more parametric approach allows well observations to be interpreted within a better constrained framework of fracture network knowledge. These DFN based simulations were supplemented by detailed geomechanical models using a hybrid FEM-DEM code that allowed the coupled stress-flow modelling of hydraulic frac interaction and pressure evolution, enabling certain stimulation design factors to be considered as well as testing the basis for the more stochastic modelling.

The benefit of these combined simulations is that a framework is developed to integrate, interpret and test all the fracture related information, allowing more guided development decisions to be made as well as identifying critical data gaps to be addressed.

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