海底油气管道泄漏事故风险分析的贝叶斯动态模型

为研究海底油气管道泄漏事故风险的动态性,预防重大事故发生,针对传统风险分析方法的局限性,基于安全屏障和事件树分析,建立海底管道泄漏事件序列模型。根据事故先兆数据和贝叶斯理论,对安全屏障的失效概率进行实时动态更新。最后,由事件树的逻辑关系得出不同泄漏场景的发生概率。结果表明:用上述的基于事件序列模型和贝叶斯理论的方法,能够克服传统风险分析方法的不足,描述不同泄漏场景发生概率随时间变化的动态特征,实现对海底管道泄漏事故风险的动态分析。     

基于贝叶斯网络的城市地下燃气管网动态风险分析

为研究城市燃气管网风险的动态性,针对传统风险分析方法的局限性,提出基于贝叶斯网络的燃气管网动态风险分析方法。构建燃气管网失效蝴蝶结模型并将其转化为贝叶斯网络模型;在事故发生状态下更新事件失效概率,识别出关键因素;根据异常事件数据和贝叶斯理论,对基本事件失效概率进行实时动态改变;随之更新管网失效及各后果发生的概率,从而实现管网的动态风险分析。研究结果表明:该方法克服了传统风险分析方法的不足,可动态反映燃气管网失效和事故后果发生概率随时间变化的特征,能够为城市地下燃气管网的风险分析与事故预防提供参考。     

不完全信息条件下的FPSO油气泄漏风险分析

为解决数据稀缺情况下的浮式生产系统(FPSO)油气泄漏重大事故风险评价问题,引入新的层次贝叶斯风险分析(HBA)方法。首先,基于安全屏障和事件树分析,建立FPSO油气泄漏事件序列模型;其次,根据事故先兆数据和贝叶斯推断,对安全屏障的先验分布增加一层估计,得到FPSO油气泄漏连锁事故的发生概率;最后,通过实时更新屏障失效概率,实现对FPSO事故风险的动态定量分析。结果表明:HBA法可以充分利用稀缺数据,并从相关数据中添加先验信息,得到各参数的后验概率,并据此确定,初始的油气泄漏大多会演变为大范围泄漏和小范围火灾。     

基于UHBs的输气管道高后果区泄漏燃爆事故分析

为探究输气管道高后果区中人的不安全行为（Unsafe Human Behaviors,UHBs）对输气管道泄漏燃爆事故发生的影响,结合模糊Bow-tie模型和贝叶斯网络对输气管道泄漏燃爆事故进行分析。构建基于T-S模糊故障树的输气管道泄漏燃爆模糊Bow-tie模型,并转化为贝叶斯网络;从人的不安全行为发生的可能性出发,将不同等级高后果区划分为不同等级人口敏感区;利用专家经验评判法得到不同等级人口敏感区基本事件的先验概率和中间事件的条件概率表;运用贝叶斯网络双向推理算法求解模糊Bow-tie模型。结果表明:随着地区人口敏感等级的提高,输气管道泄漏燃爆事故发生的概率随之增大,发现导致输气管道失效泄漏事故发生的最主要原因为施工破坏,失效原因与EGIG分析的结果基本相符,验证该方法在高后果区输气管道泄漏燃爆事故分析上的可行性,可为输气管道高后果区的安全管理提供决策依据。     

Fault Tree+软件在长输天然气管道定量风险分析中的应用

为了确定影响长输天然气管道系统发生事故的各种因素,减少事故发生所造成的损失,保证管道安全运行,借助Fault Tree+软件对天然气管道系统进行了定量风险分析.根据相关资料,建立了长输天然气管道系统的故障树,明确了44个基本事件的概率,并运用Fault Tree+软件分析、计算功能,确定了管道穿孔、断裂等中间事件以及管道失效顶事件的概率.再以“断裂泄漏”事故开展事件树分析,建立“立即点燃”、“延迟点燃”等事件,并推导了可能导致的所有后果及概率,计算出“延迟点燃”事件没有发生以及发生“爆炸”事故的概率.最后以经济损失来度量“断裂泄漏”各后果事件的失效风险,累加每个后果事件的经济损失,得到某段天然气管道“断裂泄漏”事故的风险值.     

危险化学品泄漏事故演化机理研究

为了分析危险化学品泄漏事故演化机理,简化其定量风险分析过程,先假设容器失效导致的初始事件为危险化学品泄漏事件,再根据危险化学品物质特性分类和中间演化事件场景,构建了常见的各类危险化学品发生泄漏初始事件后的通用事件树,并根据已有的研究成果,对事件树中各种中间演化事件概率进行取值研究。研究结果表明,通用事件树能很好地揭示基于物质特性和中间事件场景的泄漏事故演化规律,应用含中间演化事件概率的通用事件树能计算出各种事故后果发生的频率,对危险化学品事故的快速定量风险分析有十分重要的参考意义。     

基于动态贝叶斯网络的城市综合管廊燃气泄漏动态风险评价

为解决综合管廊燃气管网系统风险因素多、风险状态随时间动态变化等问题，在传统故障树和静态贝叶斯网络等方法的基础上提出了基于动态贝叶斯网络的城市综合管廊燃气泄漏动态风险评价方法。首先利用蝴蝶结模型分析总结了导致综合管廊燃气管网发生泄漏的主要风险源和不同事故后果。然后，引入时间因素与Leaky Noisy-or Gate模型，根据故障树模型的映射规则，建立城市综合管廊燃气泄漏的动态贝叶斯网络模型。最后，利用动态贝叶斯网络的双向推理功能对模型进行求解。由实例分析得到了某综合管廊燃气泄漏概率及各事故后果概率的时序变化曲线，通过反向推理得到了导致燃气泄漏的主要风险源。研究成果可为综合管廊的风险评估、日常维护提供理论支持。     

基于贝叶斯—LOPA方法的深水钻井安全屏障可靠性分析

据统计,钻井过程是海上井喷事故的高发阶段,为保障钻井安全进行,安装了节流管线、防喷器等安全屏障减缓系统风险,安全屏障的可靠性直接影响钻井安全。利用贝叶斯—LOPA方法建立深水钻井安全屏障可靠性分析模型,以储层-溢流-井涌-井喷为事件链,运用三级井控理论建立独立保护层,每一保护层运用贝叶斯网络方法计算失效概率。在GeNIe软件中完成模型建立,并且完成后验概率计算、敏感性分析、影响力分析。通过分析计算结果给出风险控制关键事件和风险减缓措施,为钻井现场安全工作提出指导意见,确保深水钻井安全有序进行。     

基于故障树与贝叶斯网络的钻井井塌事故的定量分析

井塌事故在钻井过程极其常见,由于钻井液、井身结构等设计不合理,以及钻井过程的误操作均会造成井塌的发生,其后果主要是造成卡钻、卡套管等事故,严重时造成井眼毁坏。为了对井塌事故进行危险性分析,采用贝叶斯网络与故障树相结合的方法,避免传统故障树方法的局限性。首先对井塌事故的所有危险因素进行辨识建立故障树,然后将故障树转化为贝叶斯网络,建立条件概率表,运用贝叶斯网络的推理能力对井塌事故进行危险性分析。对井塌事故各基本事件概率分布的计算分析结果表明,考虑事件的多态性,综合利用故障树分析法和贝叶斯网络法能有效提高井塌事故分析的有效性,推算出井塌事故概率分布更为准确。     

基于前导事件和贝叶斯网络的储罐溢油动态风险评价

部分高风险危化品企业搬迁改造困难重重,为控制风险、保护周围人民生命和财产安全,有必要建立动态风险评价系统,对事故发生概率进行监控和预测。采用贝叶斯网络对事故发生概率进行定量分析。先在利用蝴蝶结模型辨识事故原因和后果的基础上,将其转化为贝叶斯网络模型;再导入"前导事件"信息和先验概率推导后验事故发生概率,量化分析事故发生随时间的变化概率;最后,以储罐溢流场景为例进行动态风险计算,结果表明,随化工装置生产时间和"前导事件"增长,元件失效概率和事故风险呈显著增长趋势。因此建议企业应重视"前导事件"并采取措施减少"前导事件",如优化检维修方案、及时更换关键部件、进行全面的事故调查等。     

Dynamic accident modeling for high-sulfur natural gas gathering station

Dynamic accident modeling for a gas gathering station is implemented to prevent high-sulfur natural gas leakage and develop equipment inspection strategy. The progress of abnormal event occurring in the gas gathering station is modeled by the combination of fault tree and event sequence diagram, based on accident causal chain theory, i.e. the progress is depicted as sequential failure of safety barriers, then, the occurrence probability of the consequence of abnormal event is predicted. Consequences of abnormal events are divided into accidents and accident precursors which include incidents, near misses and so on. The Bayesian theory updates failure probability of safety barrier when a new observation (i.e. accident precursors or accidents data) arrives. Bayesian network then correspondingly updates failure probabilities of basic events of the safety barriers with the ability of abductive reasoning. Consequence occurrence probability is also updated. The results show that occurrence probability trend of different consequences and failure probability trend of safety barriers and basic events of the safety barriers can be obtained using this method. In addition, the critical basic events which play an important role in accidents occurrence are also identified. All of these provide useful information for the maintenance and inspection of the gas gathering station.     

普光天然气净化厂投料试车H2S泄漏安全管理

普光气田天然气净化厂是中国石化集团公司"川气东送工程"的关键组成部分,在投料试车期间,富含高浓度H2S的原料气决定了净化过程中的泄漏中毒高风险,尤其在投料试车过程中,实时监测现场H2S气体浓度是试车安全管理的关键。介绍了天然气净化厂净化工艺,分析了试车安全风险;利用安全监测系统、GasFindIR红外成像仪、有毒挥发气体分析仪等先进仪器设备建立了实时安全管理监测方法,实时监测现场H2S气体浓度。结果表明,该方法在天然气净化厂投料试车阶段发现H2S气体泄漏及时,泄漏点定位准确,泄漏量检测精确,保证了普光气田天然气净化厂投料试车作业安全的顺利进行,为高含H2S气田投料试车作业的安全管理提供了参考。     

A safety barrier-based accident model for offshore drilling blowouts

Blowout is one of the most serious accidents in the offshore oil and gas industry. Accident records show that most of the offshore blowouts have occurred in the drilling phase. Efficient measures to prevent, mitigate, and control offshore drilling blowouts are important for the entire offshore oil and gas industry. This article proposes a new barrier-based accident model for drilling blowouts. The model is based on the three-level well control theory, and primary and secondary well control barriers and an extra well monitoring barrier are established between the reservoir and the blowout event. The three barriers are illustrated in a graphical model that is similar to the well-known Swiss cheese model. Five additional barriers are established to mitigate and control the blowout accident, and event tree analysis is used to analyze the possible consequence chains. Based on statistical data and literature reviews, failures of each barrier are presented. These failures can be used as guidance for offshore drilling operators to become aware of the vulnerabilities of the safety barrier system, and to assess the risk related to these barriers. The Macondo accident is used as a case study to show how the new model can be used to understand the development of the events leading to the accident. The model can also be used as an aid to prevent future blowouts or to stop the escalation of events.     

Dynamic probability assessment of urban natural gas pipeline accidents considering integrated external activities

Urban gas pipelines usually have high structural vulnerability due to long service time. The locations across urban areas with high population density make the gas pipelines easily exposed to external activities. Recently, urban pipelines may also have been the target of terrorist attacks. Nevertheless, the intentional damage, i.e. terrorist attack, was seldom considered in previous risk analysis of urban gas pipelines. This work presents a dynamic risk analysis of external activities to urban gas pipelines, which integrates unintentional and intentional damage to pipelines in a unified framework. A Bayesian network mapping from the Bow-tie model is used to represent the evolution process of pipeline accidents initiating from intentional and unintentional hazards. The probabilities of basic events and safety barriers are estimated by adopting the Fuzzy set theory and hierarchical Bayesian analysis (HBA). The developed model enables assessment of the dynamic probabilities of consequences and identifies the most credible contributing factors to the risk, given observed evidence. It also captures both data and model uncertainties. Eventually, an industrial case is presented to illustrate the applicability and effectiveness of the developed methodology. It is observed that the proposed methodology helps to more accurately conduct risk assessment and management of urban natural gas pipelines.     

高压储氢系统泄漏爆炸事故的动力学演化

为研究燃料氢气泄漏、爆炸的特性和规律,预防高压储氢系统中氢气泄漏爆炸事故发生,以加氢站为背景,数值仿真45 MPa高压储罐氢气泄漏并引发爆炸事故,分析泄漏爆炸动力学性质以及爆炸波在非均匀氢气浓度中的传播机制。同时,基于泄漏爆炸事故演化的力学机理,开展氢气泄漏爆炸动态风险分析,针对氢气不同泄漏量,建立泄漏扩散形成的气云体积、气云爆炸产生的冲击波与空间x,z方向上危害距离之间关系。研究结果表明：氢气泄漏过程中,气云氢气浓度变化与流场雷诺数具有较好一致性;氢气扩散受到高压储氢罐周围装置影响,流场中氢气浓度分布不均匀;当发生燃烧爆炸事故时,冲击波参数和湍动能变化梯度大;得到复杂布局区域冲击波超压峰值与比例距离之间关系式,其相比于理论方法更精细、计算结果更准确。研究结果可为降低高压储氢系统泄漏爆炸事故后果、采取有效防护措施提供一定依据。     

Application of dynamic risk analysis in offshore drilling processes

Process safety is the common global language used to communicate the strategies of hazard identification, risk assessment and safety management. Process safety is identified as an integral part of process development and focuses on preventing and mitigating major process accidents such as fires, explosions, and toxic releases in process industries. Accident probability estimation is the most vital step to all quantitative risk assessment methods. Drilling process for oil is a hazardous operation and hence safety is one of the major concerns and is often measured in terms of risk. Dynamic risk assessment method is meant to reassess risk in terms of updating initial failure probabilities of events and safety barriers, as new information are made available during a specific operation. In this study, a Bayesian network model is developed to represent a well kick scenario. The concept of dynamic environment is incorporated by feeding the real-time failure probability values (observed at different time intervals) of safety barriers to the Bayesian network in order to obtain the corresponding time-dependent variations in kick consequences. This study reveals the importance of real-time monitoring of safety barrier performances and quantitatively shows the effect of deterioration of barrier performance on kick consequence probabilities. The Macondo blowout incident is used to demonstrate how early warnings in barrier probability variations could have been observed and adequately managed to prevent escalation to severe consequences.     

Risk analysis of deepwater drilling operations using Bayesian network

Deepwater drilling is one of the high-risk operations in the oil and gas sector due to large uncertainties and extreme operating conditions. In the last few decades Managed Pressure Drilling Operations (MPD) and Underbalanced Drilling (UBD) have become increasingly used as alternatives to conventional drilling operations such as Overbalanced Drilling (OVD) technology. These newer techniques provide several advantages however the blowout risk during these operations is still not fully understood. Blowout is regarded as one of the most catastrophic events in offshore drilling operations; therefore implementation and maintenance of safety measures is essential to maintain risk below the acceptance criteria. This study is aimed at applying the Bayesian Network (BN) to conduct a dynamic safety analysis of deepwater MPD and UBD operations. It investigates different risk factors associated with MPD and UBD technologies, which could lead to a blowout accident. Blowout accident scenarios are investigated and the BNs are developed for MPD and UBD technologies in order to predict the probability of blowout occurrence. The main objective of this paper is to understand MPD and UBD technologies, to identify hazardous events during MPD and UBD operations, to perform failure analysis (modelling) of blowout events and to evaluate plus compare risk. Importance factor analysis in drilling operations is performed to assess contribution of each root cause to the potential accident; the results show that UBD has a higher occurrence probability of kick and blowout compared to MPD technology. The Rotating Control Devices (RCD) failure in MPD technology and increase in flow-through annulus in UBD technology are the most critical situations for kick and blowout.     

Operational risk assessment: A case of the Bhopal disaster

Accidental releases of hazardous chemicals from process facilities can cause catastrophic consequences. The Bhopal disaster resulting from a combination of inherently unsafe designs and poorly managed operations is a well-known case. Effective risk modeling approaches that provide early warnings are helpful to prevent and control such rare but catastrophic events. Probability estimation of these events is a constant challenge due to the scarcity of directly relevant data. Therefore, precursor-based methods that adopt the Bayesian theorem to update prior judgments on event probabilities using empirical data have been proposed. The updated probabilities are then integrated with consequences of varying severity to produce the risk profile.This paper proposes an operational risk assessment framework, in which a precursor-based Bayesian network approach is used for probability estimation, and loss functions are applied for consequence assessment. The estimated risk profile can be updated continuously given real-time operational data. As process facilities operate, this method integrates a failure-updating mechanism with potential consequences to generate a real-time operational risk profile. The real time risk profile is valuable in activating accident prevention and control strategies. The approach is applied to the Bhopal accident to demonstrate its applicability and effectiveness.