Hydraulic fracturing (HF) can trigger induced seismicity, but documented occurrences tend to be localized compared with the regional extent of industry operations. Factors that determine intrinsic geological susceptibility of a given region to induced seismicity remain incompletely understood. To address this uncertainty, we have developed a stochastic modeling approach to enable statistical testing of hypotheses regarding the distribution of induced seismicity. For reference, we adopted a null hypothesis that HF-induced seismic events are randomly associated with HF wells. Realizations of synthetic induced-seismicity catalogs are generated based on the Gutenberg–Richter relationship for magnitudes and explicit assumed spatial relationship(s) between HF wells and other known features, such as mapped structural corridors. Uncertainties in observed event locations and magnitudes are also considered. Based on 1000 independent realizations for each test scenario, normalized correlation coefficients, Bayesian information criteria and other statistical measures are used to quantify the similarity of synthetic catalogs to the observed seismicity distribution. We applied this approach to induced seismicity associated with HF operations within the Montney Formation, in western Canada. Three hypotheses were tested, each showing a statistically significant improvement over the null hypothesis. A previous machine-learning-based model for Seismogenic Activation Potential (SAP) showed the highest correlation between observed and synthetically generated seismicity catalogs. Our method has been developed using cloud-based computing and is easily adapted to other regions and data types.
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