基于RBI与多米诺效应的输气站场设备定量风险评价

针对目前输气站场设备风险评价方法仅以单个设备为评价对象,并未考虑设备间相互影响的特点,提出将多米诺效应分析引入输气站场设备风险评价当中,再结合RBI(基于风险检测方法)对输气站场设备进行定量风险评价。首先,计算所评价设备的失效概率与危险指数,得到设备固有风险;然后对设备的多米诺效应风险进行计算;最后,得到设备总风险。实例分析表明,考虑多米诺效应风险后,站场单个设备的风险要明显增大。     

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

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。     

基于贝叶斯网络的气井井筒完整性风险评价

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。     

Application of risk analysis in the liquefied natural gas (LNG) sector: An overview

In recent years, the global demand for liquefied natural gas (LNG) as an energy source is increasing at a very fast rate. In order to meet this demand, a large number of facilities such as platforms, FPSO (floating production, storage and offloading), FSRU (floating storage and regasification unit) and LNG ships and terminals are required for the storage, processing and transportation of LNG. Failure of any of these facilities may expose the market, companies, personnel and the environment to hazards, hence making the application of risk analysis to the LNG sector a very topical issue throughout the world. To assess the risk of accidents associated with LNG facilities and carriers, various risk analysis approaches have been employed to identify the potential hazards, calculate the probability of accidents, as well as assessing the severity of consequences. Nonetheless, literature on classification of the risk analysis models applied to LNG facilities is very limited. Therefore, to reveal the holistic issues and future perspectives on risk analysis of LNG facilities, a systematic review of the current state-of-the-art research on LNG risk analysis is necessary. The aim of this paper is to review and categorize the published literature about the problems associated with risk analysis of LNG facilities, so as to improve the understanding of stakeholders (researchers, regulators, and practitioners). To achieve this aim, scholarly articles on LNG risk analysis are identified, reviewed, and then categorized according to risk assessment methods (qualitative, semi-qualitative or quantitative; deterministic or probabilistic; conventional or dynamic), tools (ETA, FTA, FMEA/FMECA, Bayesian network), output/strategy (RBI, RBM, RBIM, facility siting, etc.), data sources (OREDA handbook, published literature, UK HSE databases, regulatory agencies' reports, industry datasets, and experts’ consultations), applications (LNG carriers and LNG fuelled ships, LNG terminals and stations, LNG offshore floating units, LNG plants), etc. Our study will not only be useful to researchers engaged in these areas but will also assist regulators, policy makers, and operators of LNG facilities to find the risk analysis models that fit their specific requirements.     

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。     

基于T-S模糊故障树的输气站场设备失效可能性研究

针对目前国内缺乏输气站场设备失效数据库的特点,同时考虑到设备具有不同的故障程度,提出将指标评价引入至T-S模糊故障树分析中,对设备进行失效可能性分析。首先构建设备的T-S模糊故障树;其次对故障树底事件进行指标评价,转化成当前工况下的故障程度;再次对设备的故障可能性值进行计算;最后,利用模糊数学的方法将故障可能性值转化为失效概率,并参考API 581中的失效概率等级对设备进行失效可能性等级划分。实例分析表明,该方法不仅比传统故障树分析更切合实际,又能够避免指标评价法淡化关键指标的不足,且兼具定量评价与半定量评价的优点。     

储气库注采管柱螺纹失效动态风险评估*

为解决储气库注采管柱螺纹失效问题,识别注采管柱螺纹失效致因与后果,基于蝴蝶结和贝叶斯网络方法构建注采管柱螺纹动态失效风险分析模型,采用模糊集理论计算模型变量先验概率,并评估注采管柱失效后果概率,从而推断注采管柱螺纹失效关键致因因素;引入先兆数据,评估注采管柱螺纹动态失效风险态势。结果表明:气体中携带固体颗粒、上螺纹速度过快、注采温度高、地层断裂等13个因素对螺纹失效风险影响较大;螺纹失效概率逐渐增大,螺纹失效后果也越来越严重,需要监控螺纹失效关键致因以降低螺纹失效的风险。     

盐穴地下储气库事故统计及风险分析

借鉴输气管道和二氧化碳地下封存设施的风险评价方法,结合盐穴地下储气库的事故统计分析,对储气库系统中的潜在风险因素进行12大类、35小类的初步分类,并归纳总结了盐穴地下储气库的3种主要事故类型。采用事故树分析的风险评价方法,对13种主要风险因素进行风险识别。在此基础上,提出定量风险评价的重要工程模型,其包括气体水合物模型、盐穴稳定性评价模型以及气体泄漏模型。该风险分析方法和工程模型有助于定量评价盐穴地下储气库的主要风险因素,为储库的安全稳定运行提供了科学依据。     

Dynamic failure probability analysis of urban gas pipeline network

The gas pipeline network is an essential infrastructure for a smart city. It provides a much-needed energy source; however, it poses a significant risk to the community. Effective risk management assists in maintaining the operational safety of the network. The risk management of the network requires reliable dynamic failure probability analysis. This paper proposes a methodology of condition monitoring and dynamic failure probability analysis of urban gas pipeline network. The methodology begins with identifying key design and operational factors responsible for pipeline failure. Subsequently, a causation-based failure model is developed as the Bowtie model. The Bowtie model is transformed into a Bayesian network, which is analyzed using operational data. The key contributory factors of accident causation are monitored. The monitored data is used to analyze the updated failure probability of the network. The gas pipeline network's dynamic failure probability is combined with the potential consequences to assess the risk. The application of the approach is demonstrated in a section of the urban gas pipeline.     

Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.     

Scenario deduction of Natech accident based on dynamic Bayesian network: A case study of landslide accident in a liquor storage tank area in Guizhou Province,China

Industrial technical accidents caused by natural disasters are defined as Natech accidents, such as earthquakes and landslides, which can cause tremendous damage to industrial storage tanks, and lead to accidental leakage and even serious fire and explosion accidents. In this study, a landslide-induced storage tank accident model under earthquake disasters was proposed, and the relationship between landslide mass impact and target impact resistance was taken into account. Also, tank failure and the formation of the pool fire were considered to be the consequences of the Natech accident. Through scenario deduction, the dynamic process of landslide Natech was transformed qualitatively into a disaster chain network diagram composed of a scenario state, a disaster-causing factor and emergency management. The Bayesian network was used to learn and deduce the parameters of the network diagram, and in this process, the prior probability and conditional probability of nodes were obtained primarily by Monte Carlo simulation, and by an improved expert scoring method based on the fuzzy set theory. Through visualization software, the sensitivity analysis of landslide Natech was achieved. Finally, a case study of a liquor storage tank area in Guizhou Province, China was carried out, and the results show that a large amount of hazardous material leakage caused by buckling is key to the formation of pool fire accidents, and several prevention measures for earthquake-induced landslide Natech was proposed according to the sensitivity analysis.     

Integrated dynamic risk assessment of buried gas pipeline leakages in urban areas

In urban areas, buried gas pipeline leakages could potentially cause numerous casualties and massive damage. Traditional static analysis and dynamic probability-based quantitative risk assessment (QRA) methods have been widely used in various industries. However, dynamic QRA methods combined with probability and consequence are rarely used to evaluate gas pipelines buried in urban areas. Therefore, an integrated dynamic risk assessment approach was proposed. First, a failure rate calculation of buried gas pipelines was performed, where the corrosion failure rate dependent on time was calculated by integrating the subset simulation method. The relationship between failure probability and failure rate was considered, and a mechanical analysis model considering the corrosion growth model and multiple loads was used. The time-independent failure rates were calculated by the modification factor methods. Next, the overall evolution process from pipeline failures to accidents was proposed, with the accident rates subsequently updated. Then, the consequences of buried gas pipeline accidents corresponding to the accident types in the evolution process were modeled and analyzed. Finally, based on the above research, dynamic calculation and assessment methods for evaluating individual and social risks were established, and an overall application example was provided to demonstrate the capacity of the proposed approach. A reliable and practical theoretical basis and supporting information are provided for the integrity and emergency management of buried gas pipelines in urban areas, considering actual operational conditions.     

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。     

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.     

基于Bayesian网络的建筑火灾疏散条件安全性评估方法*

为了对建筑火灾疏散条件安全性进行评估，基于Bayesian网络对疏散条件重要构成要素及评估方法逻辑推理过程进行研究探讨。结果表明:评估网络结构、根节点、中间节点及目标节点之间存在因果关联关系；研究得出根节点先验概率与量化节点条件概率表设定方法；Bayesian网络将风险评估与人工智能分析方法相结合，实现对建筑火灾疏散条件的安全性评估，并可用于识别高风险建筑。     

石油化工储存装置定量风险评价方法研究

提出通过基础失效概率数据库、事故树分析、事件树分析3种方式来确定重大事故的发生概率.阐述了外界气象条件和人员分布情况对风险的影响和确定方法,给出了利用区域网格方式计算装置在平面某点的个人风险叠加模型和社会风险计算方法.最后,研制开发了石油化工储存装置定量风险评价软件,利用该软件计算了某化工厂4个储存装置所产生的个人风险...     

Risk assessment of chemical process considering dynamic probability of near misses based on Bayesian theory and event tree analysis

With the development of modern automatic control systems, chemical accidents are of low frequency in most chemical plants, but once an accident happens, it often causes serious consequences. Near-misses are the precursor of accidents. As the process progresses, near misses caused by abnormal fluctuation of process variables may eventually lead to accidents. However, variables that may lead to serious consequences in the production process cannot update the risk in the life cycle of the process by traditional risk assessment methods, which do not pay enough attention to the near misses. Therefore, this paper proposed a new method based on Bayesian theory to dynamically update the probability of key variables associated with process failure risk and obtain the risk change of the near-misses. This article outlines the proposed approach and uses a chemical process of styrene production to demonstrate the application. In this chemical process, the key variables include flow rate, liquid level, pressure and temperature. In order to study the dynamic risk of the chemical process with consideration of near misses, according to the accumulated data of process variables, firstly the abnormal probability of the variables and the failure rate of safety systems associated with the variables were updated with time based on Bayesian theory. On the basis of the dynamic probability of key process variables, an event tree of possible consequences caused by variable anomalies was established. From the logical relationship of the event tree, the probability of different consequences can be obtained. The results show that the proposed risk assessment method based on Bayesian theory can overcome the shortcomings of traditional analysis methods. It shows the dynamic characteristics of the probability of different near misses, and achieves the dynamic risk analysis of chemical process accidents.     

基于贝叶斯网络的建筑火灾动态风险评估方法研究

为提高建筑火灾风险评估的准确性,建立1种智能化的动态风险评估方法。针对具体建筑的风险评估,以物联网技术为基础,构建智能消防监测系统,在建筑日常使用过程中通过动态风险评估,实现火灾风险要素的实时监测、数据传输,充分发挥大数据、云计算的支撑作用,将贝叶斯网络方法引入火灾风险定量评估过程,构建火灾动态风险评估模型;结合具体的应用实例,分析不确定因素对风险评估结果的影响。研究结果表明:基于贝叶斯网络的动态风险评估方法能较准确地反映建筑火灾风险的可能性,达到实时监测、动态评估的效果。     

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.     

基于动态贝叶斯网络的石化装置火灾事故动态风险分析

为揭示石油炼化装置事故风险动态特性和事故情景演变路径,在对石化装置进行风险因素分析的基础上构建石化装置火灾事故故障树,基于贝叶斯网络非常规突发事故的演变过程,构建情景演变下的动态贝叶斯网络模型,在综合考虑应急措施的基础上,利用MATLAB软件和联合概率公式计算出各种事故场景的状态概率.以丙烯精馏装置火灾事故为例,结果表...