CNG加气站储气井动态安全评估  

Dynamic safety assessment on gas storage wells at CNG filling stations

作　　者：俞徐超[1,2] 梁伟[1] 张来斌[1] 全恺[3] YU Xuehao;LIANG Wei;ZHANG Laibin;QUAN Kai(College of Mechanical and Transportation Engineering,China University of Petroleum（Beijing;College of Technology,Policy and Management,Technische Universiteit Delit;SINOPEC Siehuan to Eastern China Transmission Gas Pipeline Co.Ltd.)

机构地区：[1]中国石油大学(北京)机械与储运工程学院,102249 [2]代尔夫特理工大学技术政策与管理学院 [3]中国石化川气东送天然气管道有限公司

出　　处：《油气储运》2018年第9期967-974,共8页Oil & Gas Storage and Transportation

基　　金：国家重点研发计划“原油储罐的定量风险评价及分级预警技术研究”,2017YEC0805804;中国国家留学基金资助项目“油气管道风险评估动态建模与不确定性处理”,留金欧[2016]6188;中国石化科研项目“天然气压气站压缩机综合诊断与安全评价研究”,35150573-16-ZC0607-0001;中国石油大学(北京)优秀青年科研基金“系统安全预警中设备链耦合机理研究”,2462015YQ0406

摘　　要：地下储气井作为CNG加气站最主要的储气方式,其安全性是保证CNG加气站正常运行的关键。为了对CNG加气站储气井进行动态安全评估,提出一种基于贝叶斯网络的概率化动态安全评估方法:利用Bow-Tie模型建立完整的加气站储气井泄漏失效因果逻辑,根据Bow-Tie模型与贝叶斯网络的映射关系生成相应的基于贝叶斯网络的安全评估模型;分别计算顶上事件和事故后果先验概率、顶上事件发生情况下的后验概率、防锈漆完全脱落情况下的失效风险以及报警响应可靠性变化情况下的事故后果概率,并利用贝叶斯网络的特性对根节点的敏感性进行分析。动态安全评估结果表明:提高CNG加气站储气井安全性的关键在于降低高敏感性基本事件的失效概率、基于新证据周期性地预测失效风险,并通过提高报警响应可靠性以最大程度降低事故损失。(图8,表5,参20)An underground gas storage well is the most important gas storage mode at compressed natural gas （CNG） filling stations, and its safety is the key to the normal operation of CNG filling stations. In this paper, a probabilistic dynamic safety assessment method based on Bayesian network was proposed to dynamically evaluate the safety of the gas storage wells at CNG filling stations. In this method, a complete causal logic on the leakage failure of gas storage wells at CNG filling stations is established by means of Bow-Tie model, and the corresponding safety assessment model based on Bayesian network is developed according to the mapping relationship between Bow-Tie model and Bayesian network. Then, the prior probabilities of top event and accident consequence, the posterior probabilities in the case of top event, the failure risk when anti-corrosive paint is completely off and the probabilities of accident consequence when the reliability of alarm response is changed are calculated respectively. Furthermore, sensitivity of root nodes is analyzed based on the characteristics of Bayesian network. The dynamic safety assessment results show that the key to improve the safety of gas storage wells at CNG filling stations is to reduce the failure probabilities of high-sensitivity basic event, predict the failure risk periodically based on new evidences and improve the reliability of alarm response to minimize the accident loss. （8 Figures, 5 Tables, 20 References）

关 键 词：CNG 加气站 储气井 Bow-Tie模型 贝叶斯网络 动态安全评估 

分 类 号：TE832[石油与天然气工程—油气储运工程]