Characterizing production-induced anisotropy of fractured reservoirs having multiple fracture sets

Since natural fractures in petroleum reservoirs play an important role in determining fluid flow during production, knowledge of the orientation and density of fractures is required to optimize production. This paper outlines the underlying theory and implementation of a fast and efficient algorithm for upscaling a Discrete Fracture Network (DFN) to predict the fluid flow, elastic and seismic properties of fractured rocks. Potential applications for this approach are numerous and include the prediction of fluid flow, elastic and seismic properties for fractured reservoirs, model-based inversion of seismic Amplitude Versus Offset and Azimuth (AVOA) data and the optimal placement and orientation of infill wells to maximize production. Given that a single fracture network may comprise hundreds of thousands of individual fractures, the sheer size of typical DFNs has tended to limit their practical applications. This paper demonstrates that with efficient algorithms, the utility of Discrete Fracture Networks can be extended far beyond mere visualization.