Separating Hydraulic Fracturing Microseismicity From Induced Seismicity by Bayesian Inference of Non‐Linear Pressure Diffusivity

Abstract Microseismic data acquired during hydraulic stimulations is routinely used to characterize fracture (HF) propagation away from a wellbore. When the include induced seismicity (IS) related fault slip, separating HF‐related and IS‐related events essential for HF treatment optimization IS mitigation. Linear non‐linear analytical diffusivity models can be interpret microseismic quantify of HFs, but their accuracy reduces when significant microseismicity present. A Bayesian quantile regression extend these existing contaminated with IS. plausible ellipsoid filters that are clearly anomalous prior regression. The effectively separates in case study all stages without interpretative bias. This reveals faults directed connected as well those solely seismicity.