Shale gas production from organic rich shale formations is one of the most rapidly expanding areas in oil and gas exploration and production today. Because of extremely low permeability and low porosity, long horizontal wells in conjunction with multistaged massive hydraulic fracturing treatments (HFT) are required to bring economic productions from shale gas reservoirs. It has been recognized that extensive fracture networks with massive contact surface areas are necessary to support economic productions from these reservoirs. Existing natural fractures observed from borehole images (mostly mineral-filled) and the low contrast of minimum and maximum horizontal stresses are some of the key factors in creation of the post-HFT network fracture system in many shale gas reservoirs.

Currently, comprehensive design tools for hydraulic fracturing treatments of shale gas reservoirs appear not available. These tools should have the capabilities to incorporate stress field, natural fractures and lithology heterogeneity of the reservoirs and model complicated fracture networks in shale gas reservoirs. However, microseismic mapping has been widely used to monitor hydraulic fracturing job responses, to help control job execution processes, and to evaluate stimulation results. Microseismic responses reflect the collective effects of the reservoir characteristics and hydraulic fracturing treatments, and can be indicative for the productivity of the post-HFT reservoirs.

This study presents a practical methodology to model hydraulic fracturing induced fracture networks in shale gas reservoirs as a dual porosity system. This approach decouples complex reservoir characteristics and geomechanical factors from production response. Microseismic responses are used to delineate stimulated volumes from a HFT. Microseismic events and/or natural fracture intensity, along with HFT data and production history-matching analysis, provide calibration for HFT fracture intensity.  The calibrated post-HFT fracture network is crucial for production prediction.