石油钻采工艺2024，Vol.46Issue(4)：413-428,16.DOI:10.13639/j.odpt.202411051
树增强型贝叶斯模型提升溢流预警时间提前量
Enhancing kick detection lead time with a tree-augmented bayesian model
摘要
Abstract
The interval between a kick occurrence and a blowout is a critical window for preventing blowouts.To address the issues of delayed alarm times and high false alarm rates in existing Bayesian kick detection methods,improvements are necessary.This study analyzed the correlation between surface kick data to establish a tree-augmented Bayesian model.Sequential feature logging parameters were extracted from 91 drilling kick events in the Sichuan-Chongqing region to construct a training dataset.The trained model was tested to develop an early kick detection method based on the tree-augmented Bayesian network.Kick data from a well outside the training set was used as the test set for the detection model.The Bayesian-based kick detection model reduced the false alarm rate by 52.07%compared to other models.The detection model issued an alarm 16.6 minutes before the kick occurred,510 seconds earlier than the naïve Bayesian early kick detection model.The tree-augmented Bayesian early kick detection model incorporates the correlation of anomalous parameters during kick events.It significantly advances the kick detection time while maintaining a lower false alarm rate.This approach provides a new framework for developing kick detection models based on large-scale logging data.关键词
安全/钻井/大数据/人工智能/能源安全/数字化转型/工程技术/算法Key words
Safety/Drilling/Big data/Artificial intelligence/Energy Security/Digital transformation/Engineering Technology/Algorithm分类
海洋科学引用本文复制引用
王学强,樊建春,杨哲,罗双平,徐志凯,蔡正伟,熊毅..树增强型贝叶斯模型提升溢流预警时间提前量[J].石油钻采工艺,2024,46(4):413-428,16.基金项目
国家重点研发项目"陆上超深油气井井喷防控关键技术装备及示范应用"(项目编号:2023YFC3009200). （项目编号:2023YFC3009200）
