| 基于贝叶斯网络的钻进过程井漏井涌事故预警 |
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| 引用本文： | 张正,赖旭芝,陆承达,陈略峰,曹卫华,吴敏.基于贝叶斯网络的钻进过程井漏井涌事故预警[J].探矿工程,2020,47(4):114-121，144. |
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| 作者姓名： | 张正 赖旭芝 陆承达 陈略峰 曹卫华 吴敏 |
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| 作者单位： | 中国地质大学（武汉）自动化学院,中国地质大学（武汉）,中国地质大学（武汉）,中国地质大学（武汉）,中国地质大学（武汉）,中国地质大学（武汉） |
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| 基金项目： | 国家重点研发计划项目“5000米智能地质钻探技术装备研发及应用示范”课题五“智能地质钻探技术及装备仪器研制”（编号：2018YFC0603405）；国家自然科学基金重点项目“复杂地质钻进过程智能控制”（编号：61733016）；湖北省技术创新专项重大项目“复杂地质环境钻采装备关键技术开发与应用”（编号：2018AAA035） |
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| 摘 要： | 近年来，地质钻探在钻探装备和技术领域取得了长足发展，但在钻进过程事故预警方面的研究仍较欠缺。为保证钻进过程安全高效，降低事故造成的损失，本文提出了一种钻进过程井漏、井涌事故预警方法。首先，通过事故特性分析，选取表征事故特性的钻进参数。其次，考虑事故发生时钻进参数变化的不确定性，基于贝叶斯网络建立井漏、井涌事故预警模型。再者，为从含噪声的实际钻进数据中有效提取钻进参数的变化趋势，综合利用归一化、滑动平均和最小二乘线性拟合方法进行节点状态判断。最后，利用实际钻进数据对井漏、井涌事故预警模型的有效性进行验证，并对不同趋势判断界限和滑动窗口对报警性能的影响进行探讨。实验结果表明，该预警模型可对井漏、井涌事故进行有效预警，并且合适的趋势判断界限和滑动窗口可降低报警延迟，减少误报和漏报现象。
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| 关 键 词： | 地质钻探 钻进过程 事故预警 贝叶斯网络 井漏 井涌 |
| 收稿时间： | 2020/2/17 0:00:00 |
| 修稿时间： | 2020/3/30 0:00:00 |
Lost circulation and kick accidents warning based on Bayesian network for the drilling process |
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| ZHANG Zheng,LAI Xuzhi,LU Chengd,CHEN Luefeng,CAO Weihua and WU Min.Lost circulation and kick accidents warning based on Bayesian network for the drilling process[J].Exploration Engineering(Drilling & Tunneling),2020,47(4):114-121，144. |
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| Authors: | ZHANG Zheng LAI Xuzhi LU Chengd CHEN Luefeng CAO Weihua and WU Min |
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| Institution: | School of Automation, China University of Geosciences,China University of Geosciences, Wuhan,China University of Geosciences, Wuhan,China University of Geosciences, Wuhan,China University of Geosciences, Wuhan,China University of Geosciences, Wuhan |
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| Abstract: | In recent years, geological drilling has made great progress in the field of drilling equipment and technology, but research on drilling accidents warning is still insufficient. To ensure safety and efficiency of the drilling process and reduce the losses caused by accidents, a lost circulation and kick accidents warning method for the drilling process is proposed in this paper. First, the drilling parameters that characterize accidents are selected through the analysis of accidents. Second, considering the uncertainty of the changes of drilling parameters when an accident occurs, a lost circulation and kick accidents warning model is established based on Bayesian network. Third, to effectively extract the trend of drilling parameters from actual drilling data with noises, the normalization, moving average and least square linear fitting methods are jointly used to judge the node status. Finally, the actual drilling data is used to verify the lost circulation and kick accidents warning model. The influence of different trend judgment boundaries and moving windows on the alarm performance is discussed. The experimental results show that the warning model effectively provides warning of the lost circulation and kick accidents. And a proper trend judgment boundary and moving window can reduce alarm delay, false alarm and missed alarm. |
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| Keywords: | geological drilling drilling process accidents warning Bayesian network lost circulation kick |
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