To determine the degree of connectivity and complexity of a stimulated fracture network, a prescriptive completion program was undertaken in the Horn River Shale Basin which enabled continuous monitoring of pressure interactions among horizontal wells during multi-stage hydraulic fracturing. This paper introduces a novel approach to characterize the stimulated fracture network, and consequently, to optimize the stimulation, wellbore placement, and re-fracturing designs, by integrating the pressure hits captured from passive wellbores on a pad during fracturing operations. If effective, it may also provide a cost-effective alternative to microseismic monitoring.
The workflow initially considers a rigorous data analysis on available pressure hits at each frac-stage in time and space, including the location of pressure events, time of flights to offsetting stimulation, and the magnitude and intensity of pressure hits/falloffs. Streamline simulation, assisted with a hydraulic fracturing module, is then used to match the pressure hits/falloffs in the passive wells. This ultimately provides a dynamic probabilistic 3D map of the fracture network growth, reservoir complexity and inter-well connectivity. The fundamental mechanisms of hydraulic fracturing and the interactions across natural and induced fractures (fracture initiation/propagation/growth) are implemented by means of an advanced coupled hydro-mechanical code, based on distinct element method.
Results from initial data analyses were fed into a hydro-mechanical model, which incorporated the physics of the hydraulic fracturing process, in order to reproduce the pressure hit signatures. An assisted streamline-based technique was used to simulate various scenarios of pressure hit responses to construct a database of standard pressure hit/falloff patterns. This database, compiled into a dynamic 3D map, facilitated a probabilistic approach to calculate a robust estimate range of stimulated fracture network of the pad area. This database can be subsequently used in future stimulation and re-fracturing designs. The backbone of the highly complex fracture network, extracted from pressure hit/falloff data, was found to closely align with high-resolution microseismic data.
Calibration of the hydro-mechanical model using the pressure hit data provides increased confidence in the use of the model to optimize well placement and hydraulic fracturing designs. In the absence of microseismic data, this unique workflow has the potential to deliver real-time on-site monitoring of fracturing operation at a reduced cost and acceptable accuracy, to provide additional statistics on complexity of stimulated reservoir volume, and to offer a better assessment of the likely range of the induced fracture network among horizontal wells.