Large, high density fracture networks are necessary to deliver commercial production rates from sub-microdarcy permeability organic-rich shale reservoirs. Operators have increased lateral length and fracture stages as the primary means to improve well performance and, more recently, are tailoring completion techniques to local experience and reservoir-specific learning. In particular, closer fracture stage spacing or increased number of stages per well have driven improvements in well performance. Large scale adoption occurs when the change in performance is clearly linked to the reservoir-specific completion design.
Horizontal well fracturing efficiency in unconventional reservoirs is notoriously poor. Numerous authors report that 40 to 60 per cent of frac stages or individual perforation clusters have been shown (albeit with highly uncertain surveillance methods) to contribute little or no production. The fracture initiation and propagation process is very complex in shale; it is affected by in-situ stress, geomechanical heterogeneity, presence of natural fractures, and completion parameters. Close cluster spacing can provide enhanced well production; however, if the spacing is too close, stress shadowing among these clusters can actually induce higher stresses, creating fracture competition.
This paper presents an approach to the integration of these parameters through both state-of-the-art geological characterization and unconventional 3D hydraulic fracture modeling. We couple stochastic discrete fracture network (DFN) models of in-situ natural fractures with a state-of-the art 3D unconventional fracture simulator. The modeled fracture geometry and associated conductivity is exported into a dynamic reservoir flow model, for production performance prediction. Calibrated toolkits and workflows, underpinned by integrated surveillance including distributed temperature and acoustic fiber optic sensing (DTS/DAS), are used to optimize horizontal well completions. A case study is presented which demonstrates the technical merits and economic benefits of using this multidisciplinary approach to completion optimization.