Natural fractures can reactivate during hydraulic stimulation and interact with hydraulic fractures producing a complex and highly productive natural-hydraulic fracture network. This phenomenon and the quality of the resulting productive stimulated rock volume (P-SRV) is primarily a function of the natural fracture network characteristics (e.g. spacing, height, length, number of fracture sets, orientation and frictional properties); in-situ stress state (e.g. stress anisotropy and magnitude); stimulation design parameters (e.g. pumping schedule, the type/volume of fluid(s) and proppant), well architecture (number and spacing of stages, perforation length, well orientation), and the physics of the natural-hydraulic fracture interaction (e.g. cross-over, arrest, reactivation). Geomechanical models can quantify the impact of key parameters that control the extent and complexity of the P-SRV, with implications to stimulation design and well optimization in the field. In this paper we present a series of geomechanical simulations to predict natural-hydraulic fracture interaction and the resulting fracture network in complex settings. A geomechanics based sensitivity analysis is performed that integrates key reservoir-geomechanical parameters to forward model complex fracture network generation, synthetic microseismic (MS) response, and associated conductivity paths as they evolve during stimulation operations. The simulations test two different natural-hydraulic fracture interaction scenarios and can generate synthetic MS events. Our sensitivity analysis shows that geomechanical models that involve complex fracture networks can be calibrated against MS data and helps predict reservoir response to stimulation, and optimize P-SRV. We analyze a field data set (obtained from two hydraulically fractured wells in the Barnett formation, Tarrant County, TX) and establish coupling between geomechanics and MS within the framework of a 3D geological model. This coupling provides a mechanics based approach to (i) verify MS trends and anomalies in the field (ii) optimize P-SRV for reservoir simulations (iii) improve stimulation design on the current well in near realtime and well design/stimulation for future wells.
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