Petroleum Science >2023,??Issue4:??- DOI: https://doi.org/10.1016/j.petsci.2023.02.003
A machine learning-based study of multifactor susceptibility and risk control of induced seismicity in unconventional reservoirs Open?Access
文章信息
作者：Gang Hui, Zhang-Xin Chen, Hai Wang, Zhao-Jie Song, Shu-Hua Wang, Hong-Liang Zhang, Dong-Mei Zhang, Fei Gu
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投稿時(shí)間：
引用方式：Gang Hui, Zhang-Xin Chen, Hai Wang, Zhao-Jie Song, Shu-Hua Wang, Hong-Liang Zhang, Dong-Mei Zhang, Fei Gu, A machine learning-based study of multifactor susceptibility and risk control of induced seismicity in unconventional reservoirs, Petroleum Science, Volume 20, Issue 4, 2023, Pages 2232-2243, https://doi.org/10.1016/j.petsci.2023.02.003.
文章摘要
Abstract: A comprehensive dataset from 594 fracturing wells throughout the Duvernay Formation near Fox Creek, Alberta, is collected to quantify the influences of geological, geomechanical, and operational features on the distribution and magnitude of hydraulic fracturing-induced seismicity. An integrated machine learning-based investigation is conducted to systematically evaluate multiple factors that contribute to induced seismicity. Feature importance indicates that a distance to fault, a distance to basement, minimum principal stress, cumulative fluid injection, initial formation pressure, and the number of fracturing stages are among significant model predictors. Our seismicity prediction map matches the observed spatial seismicity, and the prediction model successfully guides the fracturing job size of a new well to reduce seismicity risks. This study can apply to mitigating potential seismicity risks in other seismicity-frequent regions.
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Keywords: Induced seismicity; Hydraulic fracturing; Seismicity susceptibility; Risk control; Machine learning