摘要：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. ? 2023 The Authors

ISSN号：1672-5107

卷、期、页：卷20期4：2232-2243

发表日期：2023-08-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,CSCD（中国科技引文期刊）（核心）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Petroleum Science

参与作者：王海,王舒华,张洪亮,张冬梅,顾斐

第一作者：惠钢,CHEN ZHANGXING,宋兆杰

论文类型：期刊论文

论文概要：惠钢,CHEN ZHANGXING,王海,宋兆杰,王舒华,张洪亮,张冬梅,顾斐，A machine learning-based study of multifactor susceptibility and risk control of induced seismicity in unconventional reservoirs，Petroleum Science，2023，卷20期4：2232-2243

论文题目：A machine learning-based study of multifactor susceptibility and risk control of induced seismicity in unconventional reservoirs