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[1]张弘,申瑞臣,袁光杰,等.基于贝叶斯网络的气井井筒完整性风险评价[J].中国安全生产科学技术,2017,13(9):132-138.[doi:10.11731/j.issn.1673-193x.2017.09.021]
　ZHANG Hong,SHEN Ruichen,YUAN Guangjie,et al.Risk evaluation on wellbore integrity of gas well based on Bayesian network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(9):132-138.[doi:10.11731/j.issn.1673-193x.2017.09.021]
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基于贝叶斯网络的气井井筒完整性风险评价
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
13
期数:
2017年9期
页码:
132-138
栏目:
现代职业安全卫生管理与技术
出版日期:
2017-09-30

文章信息/Info

Title:
Risk evaluation on wellbore integrity of gas well based on Bayesian network
文章编号:
1673-193X(2017)-09-0132-07
作者:
张弘1; 2; 申瑞臣1; 袁光杰1; 胡耀方1
(1. 中国石油集团钻井工程技术研究院，北京 102206；2. 中国石油勘探开发研究院，北京 100083)
Author(s):
ZHANG Hong1; 2;  SHEN Ruichen1;  YUAN Guangjie1;  HU Yaofang1
(1. CNPC Drilling Research Institute, Beijing 102206, China; 2. Research Institute of Petroleum Exploration and Development, Beijing 100083, China)
关键词:
贝叶斯网络; 井筒完整性; 风险评价; Noisy OR gate模型; 概率分析
Keywords:
Bayesian network;  wellbore integrity;  risk evaluation;  Noisy-OR gate model;  probability analysis
分类号:
TE38
DOI:
10.11731/j.issn.1673-193x.2017.09.021
文献标志码:
A
摘要:
井筒完整性失效是气井生产中的主要风险，为有效评价井筒完整性风险，应用贝叶斯网络的推理与学习能力，建立了基于贝叶斯网络和Noisy-OR gate模型的井筒完整性失效概率计算方法和风险评价模型。由故障树分析将井筒分为管柱、水泥环密封性、井口装置、水力屏障和其他部件5个评价单元，确定了各单元的主要风险因素，建立了井筒完整性失效的贝叶斯网络拓扑结构；由Noisy-OR gate模型和历史数据，确定了贝叶斯网络的条件概率参数；将基于贝叶斯网络的失效概率与层次分析法相结合，确定了风险评价指标和等级划分标准;建立了气井井筒完整性风险评价方法。结果表明，该方法实现了井筒完整性失效概率的定量计算、风险的定量评价和主要风险因素的反向推理,可为预防和控制井筒完整性失效提供决策依据，有助于降低井筒完整性失效风险。
Abstract:
The failure of wellbore integrity is one of the major risks during the production of gas well. To evaluate the risk of wellbore integrity effectively, by applying the reasoning and learning ability of Bayesian network, the calculation method on failure probability and the risk evaluation model of wellbore integrity based on Bayesian network and the Noisy-OR gate model were established. According to the fault tree analysis (FTA), the wellbore was divided into five evaluation units: string, sealing of cement sheath, wellhead installations, hydraulic barrier and other parts, and the main risk factors of each unit were determined. Thus, the topology structure of Bayesian network for well integrity failure was built. Based on the Noisy-OR gate model and the historical data, the conditional probability parameters of Bayesian network were determined. The risk evaluation indexes and grading standard were determined by combining the failure probability from Bayesian network and the analytical hierarchy process (AHP) method. Thereby, the risk evaluation method of wellbore integrity for gas well was formed. The results showed that the quantitative calculation of failure probability, the quantitative evaluation of risk, and the backward reasoning of main risk factors for wellbore integrity can be achieved by using this method. It can provide decision basis for the prevention and control of well integrity failure, and help to reduce the risk of well integrity failure.

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备注/Memo

备注/Memo:
中国石油集团公司课题（2015E-400602，2015E-4003）
更新日期/Last Update: 2017-10-12