Analysis of the influential factors controlling the occurrence of injection-induced earthquakes in Northeast British Columbia, Canada, using machine-learning-based algorithms

Abstract

We use a machine learning approach based on the Extreme Gradient Boosting algorithm to analyze the controlling factors of injection induced earthquakes (IIE) in northeast British Columbia (NE BC), particularly hydraulic fracturing induced seismicity within the Montney unconventional resource play. We compile comprehensive datasets incorporating seismological, operational, and stratigraphic features corresponding to various spatiotemporal grids to rank the controlling factors in terms of their importance. Our results show that, in general, the number of hydraulic fracturing stage is the most important factor controlling IIE, followed by cumulative volume of injected fluid and two depth-related features: depth difference between hydraulic fracturing injection and Montney formation and depth difference between Precambrian basement and Montney formation. Out of 168 datasets with varying spatiotemporal grids, the best model performance is achieved for the dataset with spatial grid of 0.1 degree and temporal grid of 10 days. Based on the Shapley Additive exPlanations values, we observe that a threshold of ~ 2200 in the number of hydraulic fracturing stages significantly encourages the occurrence of IIE while the threshold for cumulative volume of injected fluid is ~ 500,000 m³. Moreover, the occurrence of IIE is mostly encouraged when injections occur within ~ 300 m below the top of Montney formation (associated with the maximum thickness of this formation in NE BC) and at ~ 1700 m above the top of Precambrian basement.