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

Microseismic data acquired during hydraulic stimulations is routinely used to characterize hydraulic fracture (HF) propagation away from a wellbore. When the data include induced seismicity (IS) related to induced fault slip, separating HF-related and IS-related events is essential for HF treatment optimization and IS mitigation. Linear and non-linear analytical diffusivity models can be used to interpret microseismic data and quantify the propagation of HFs, but their accuracy reduces when significant induced microseismicity is present. A Bayesian quantile regression is used to extend these existing diffusivity models to data contaminated with IS. A plausible ellipsoid filters events that are clearly anomalous prior to the quantile regression. The regression effectively separates HF-related microseismic events from induced events in a case study for all stages without interpretative bias. This reveals faults that are directed connected to HFs, as well as those solely related with induced seismicity. Plain Language Summary We present a method for analyzing point clouds of small seismic events observed during hydraulic fracturing (microseismicity). The method separates events that are closely related with hydraulic fractures from those that are anomalous, far from hydraulic fractures, or triggered on preexisting faults. The method uses a statistical regression to infer the diffusivity, or rate of propagation, of hydraulic fractures. We account for a prior expectation of how quickly the hydraulic fracture propagates in length, width, and height. Events occurring beyond the inferred hydraulic fracture diffusivity are classified as induced seismicity. A case study is reviewed, and important parts of the regression are discussed. By separating hydraulic fracture related microseismicity from triggered events, it is possible to clearly understand each and how they relate to each other in a data-driven, automated way.