Abstract

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 conductive reservoir area are primarily functions 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., crossover, arrest, reactivation). Geomechanical models can quantify the impact of key parameters that control the extent and complexity of the conductive reservoir area, with implications to stimulation design and well optimization in the field. We have developed a series of geomechanical simulations to predict natural-hydraulic fracture interaction and the resulting fracture network in complex settings. A geomechanics-based sensitivity analysis was performed that integrated 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 tested two different natural-hydraulic fracture interaction scenarios and could generate synthetic MS events. The sensitivity analysis revealed that geomechanical models that involve complex fracture networks can be calibrated against MS data and can help to predict the reservoir response to stimulation and optimize the conductive reservoir area. We analyzed a field data set (obtained from two hydraulically fractured wells in the Barnett Formation, Tarrant County, Texas) and established a coupling between the geomechanics and MS within the framework of a 3D geologic model. This coupling provides a mechanics-based approach to (1) verify MS trends and anomalies in the field, (2) optimize conductive reservoir area for reservoir simulations, and (3) improve stimulation design on the current well in near-real-time and well design/stimulation for future wells.

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