Machine Learning-Based Analysis of Geological Susceptibility to Induced Seismicity in the Montney Formation, Canada

Abstract

We analyze data from 6,466 multistage horizontal hydraulic fracturing wells drilled into the Montney Formation over a large region in western Canada to evaluate the impact of geological, geomechanical, and tectonic characteristics on the distribution of hydraulic fracturing-induced seismicity. Logistic regression was used to obtain a machine learning estimate of the seismogenic activation potential of each well. Our results fit the observed spatial variability, including an enigmatic change in seismicity at 120°W that does not correlate with any change in industrial activity. Feature importance analysis provides insight into data types that have the greatest impact on the results. Based on current data, seismogenic activation potential is most strongly influenced by depth of injection and distance of the well to the Cordilleran thrust belt.