基于贝叶斯网络的页岩气集输管道失效概率计算方法研究
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摘要
页岩气集输管道运行压力和出砂量在生产过程中衰减显著,这导致管道失效概率不断变化,针对这一问题,采用贝叶斯网络方法,建立了页岩气集输管道失效概率动态计算模型。首先,分析页岩气气质特征、管道运行工况及失效原因,利用逻辑门的连接关系,建立了页岩气集输管道失效故障树;其次,基于贝叶斯网络与失效故障树的结构映射关系,将失效故障树转化成贝叶斯网络结构;然后,通过贝叶斯网络的参数学习,实现模型求解;最后,进行了实例应用。研究结果表明:该模型不仅可有效计算页岩气集输管道的失效概率,还能确定影响管道失效的关键风险因素,并且可通过调整节点的状态及概率分布,实现页岩气集输管道失效概率的更新。
To solve the problem that the failure probability of pipeline is constantly changing caused by the significant decrease of operating pressure and sand production rate during the working process of the gathering pipeline of shale gas,a dynamic calculation model for the failure probability of the gathering pipeline of shale gas was established by using the Bayesian network method. Firstly,the gas quality characteristics of shale gas as well as the operating conditions and failure reasons of pipeline were analyzed,and the failure fault tree for the gathering pipeline of shale gas was established by using the connection relationship of the logic gate. Secondly,the failure fault tree was transformed into the Bayesian network structure based on the structure mapping relationship between the Bayesian network and the failure fault tree. Then the model solving was realized through the parameters learning of Bayesian network. Finally,the example application was carried out. The results showed that the model could not only calculate the failure probability of the gathering pipeline of shale gas effectively,but also determine the key risk factors affecting the failure of pipeline. The failure probability of the gathering pipeline of shale gas could be updated through adjusting the state and probability distribution of the nodes.
To solve the problem that the failure probability of pipeline is constantly changing caused by the significant decrease of operating pressure and sand production rate during the working process of the gathering pipeline of shale gas,a dynamic calculation model for the failure probability of the gathering pipeline of shale gas was established by using the Bayesian network method. Firstly,the gas quality characteristics of shale gas as well as the operating conditions and failure reasons of pipeline were analyzed,and the failure fault tree for the gathering pipeline of shale gas was established by using the connection relationship of the logic gate. Secondly,the failure fault tree was transformed into the Bayesian network structure based on the structure mapping relationship between the Bayesian network and the failure fault tree. Then the model solving was realized through the parameters learning of Bayesian network. Finally,the example application was carried out. The results showed that the model could not only calculate the failure probability of the gathering pipeline of shale gas effectively,but also determine the key risk factors affecting the failure of pipeline. The failure probability of the gathering pipeline of shale gas could be updated through adjusting the state and probability distribution of the nodes.
引文
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[15]刘健,朱元坤,秦浩志,等.基于贝叶斯网络水下采油树系统泄漏风险分析[J].润滑与密封,2018,43(1):109-115.LIU Jian,ZHU Yuankun,QIN Haozhi,et al. Leakage risk analysis of underwater tree system based on Bayesian network[J]. Lubrication and Dense,2018,43(1):109-115.
[2] GAO J,HE C,YOU F. Shale gas process and supply chain optimization[Z]. Advances in Energy Systems Engineering. Springer International Publishing,2017.
[3]司光,林好宾,丁丹红,等.页岩气水平井工厂化作业造价确定与控制对策[J].天然气工业,2013,33(12):163-167.SI Guang,LIN Haobin,DING Danhong,et al. The determination and control measures of the factory operation cost of shale gas horizontal well[J]. Natural Gas Industry,2013,33(12):163-167.
[4]唐颖,唐玄,王广源,等.页岩气开发水力压裂技术综述[J].地质通报2011,30(S1)393-399.TANG Ying,TANG Xuan,WANG Guangyuan,et al. Review of shale gas development hydraulic fracturing technology[J]. Geological Bulletin 2011,30(S1):393-399.
[5]吴瑕,李长俊,贾文龙.领结法及其在工业领域中的应用[J].油气储运,2017,36(6):657-664.WU Xia,LI Changjun,JIA Wenlong. Bow tie method and its application in industrial field[J]. Oil and Gas Storage and Transportation,2017,36(6):657-664.
[6]王文和,沈溃领,王骏逸,等.油气管道失效概率计算模型及其应用研究进展[J].化工设备与管道,2016,53(6):71-76.WANG Wenhe,SHEN Kuiling,WANG Junyi,et al. Research progress on failure probability calculation model and its application in oil and gas pipelines[J]. Chemical Equipment and Pipelines,2016,53(6):71-76.
[7]郝永梅,邢志祥,王凯全,等.天然气管道失效的定量风险分析[J].油气储运,2011,30(4):263-265.HAO Yongmei,XING Zhixiang,WANG Kaiquan,et al. Quantitative risk analysis of natural gas pipeline failure[J]. Oil and Gas Storage and Transportation,2011,30(4):263-265.
[8] LI X H,CHEN G M,ZHU H W. Quantitative risk analysis on leakage failure of submarine oil and gas pipelines using Bayesian network[J]. Process Safety and Environmental Protection,2016,103:163-173.
[9] KHAKZAD N,KHAN F,AMYOTTE P. Safety analysis in process facilities:Comparison of fault tree and Bayesian network approaches[J]. Reliability Engineering and System Safety,2011,96(8):925-932.
[10]顾建荣.陆上油气管道急剧危害定量风险评价技术研究[D].西安:西安建筑科技大学,2016.
[11] WU J S,ZHOU R,XU S D,et al. Probabilistic analysis of natural gas pipeline network accident based on Bayesian network[J]. Journal of Loss Prevention in the Process Industries, 2017, 46:126-136.
[12]马欣,师统麾,薛涛,等.基于贝叶斯网络的LNG储罐泄漏事故树改进研究[J].现代化工,2017,37(4):185-188.MA Xin,SHI Tonghui,XUE Tao,et al. Improvement of LNG tank leakage accident tree based on Bayesian network[J]. Modern Chemical Engineering,2017,37(4):185-188.
[13] WANG W H,SHEN K L,WANG B,et al. Failure probability analysis of the urban buried gas pipelines using Bayesian networks[J]. Process Safety and Environmental Protection,2017,111:678-686.
[14]李江飞.基于贝叶斯网络的地铁项目施工风险评价研究[D].哈尔滨:哈尔滨工业大学,2013.
[15]刘健,朱元坤,秦浩志,等.基于贝叶斯网络水下采油树系统泄漏风险分析[J].润滑与密封,2018,43(1):109-115.LIU Jian,ZHU Yuankun,QIN Haozhi,et al. Leakage risk analysis of underwater tree system based on Bayesian network[J]. Lubrication and Dense,2018,43(1):109-115.