Journal
GEOPHYSICAL RESEARCH LETTERS
Volume 47, Issue 22, Pages -Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2020GL089651
Keywords
induced seismicity; seismogenic potential; supervised machine learning
Categories
Funding
- Microseismic Industry Consortium
- Geoscience BC
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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 degrees 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.
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