Analytical and Machine-LearningAnalysis of Hydraulic Fracture-Induced Natural Fracture Slip

Summary Stimulated reservoir volume (SRV) is a prime factor controlling well performance in unconventional shale plays. In general, SRV describes the extent of connected conductive fracture networks within the formation. Being a pre-existing weak interface, natural fractures (NFs) are the preferred failure paths. Therefore, the interaction of hydraulic fractures (HFs) and NFs is fundamental to fracture growth in a formation. Field observations of induced fracture systems have suggested complex failure zones occurring in the vicinity of HFs, which makes characterizing the SRV a significant challenge. Thus, this work uses a broad range of subsurface conditions to investigate the near-tip processes and to rank their influences on HF-NF interaction. In this study, a 2D analytical workflow is presented that delineates the potential slip zone (PSZ) induced by a HF. The explicit description of failure modes in the near-tip region explains possible mechanisms of fracture complexity observed in the field. The parametric analysis shows varying influences of HF-NF relative angle, stress state, net pressure, frictional coefficient, and HF length to the NF slip. This work analytically proves that an NF at a 30 ± 5° relative angle to an HF has the highest potential to be reactivated, which dominantly depends on the frictional coefficient of the interface. The spatial extension of the PSZ normal to the HF converges as the fracture propagates away and exhibits asymmetry depending on the relative angle. Then a machine-learning (ML) model [random forest (RF) regression] is built to replicate the physics-based model and statistically investigate parametric influences on NF slips. The ML model finds statistical significance of the predicting features in the order of relative angle between HF and NF, fracture gradient, frictional coefficient of the NF, overpressure index, stress differential, formation depth, and net pressure. The ML result is compared with sensitivity analysis and provides a new perspective on HF-NF interaction using statistical measures. The importance of formation depth on HF-NF interaction is stressed in both the physics-based and data-driven models, thus providing insight for field development of stacked resource plays. The proposed concept of PSZ can be used to measure and compare the intensity of HF-NF interactions at various geological settings.