Quantitative risk analysis of fire and explosion on the top-side LNG-liquefaction process of LNG-FPSO

Since the massive use and production of fuel oil and natural gas, the excavating locations of buried energy-carrying material are moving further away from onshore, eventually requiring floating production systems like floating production, storage and offloading (FPSO). Among those platforms, LNG-FPSO will play a leading role to satisfy the global demands for the natural gas in near future; the LNG-FPSO system is designed to deal with all the LNG processing activities, near the gas field. However, even a single disaster on an offshore plant would put the whole business into danger. In this research, the risk of fire and explosion in the LNG-FPSO is assessed by quantitative risk analysis, including frequency and consequence analyses, focusing on the LNG liquefaction process (DMR cycle). The consequence analysis is modeled by using a popular analysis tool PHAST. To assess the risk of this system, 5 release model scenarios are set for the LNG and refrigerant leakages from valves, selected as the most probable scenarios causing fire and explosion. From the results, it is found that the introduction of additional protection methods to reduce the effect of fire and explosion under ALARP criteria is not required, and two cases of the selection of independent protection layers are recommended to meet the SIL level of failure rate for safer design and operation in the offshore environment.     

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.     

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...     

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.     

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.     

Backtracking and prospect on LNG supply chain safety

The safety issues of Liquefied Natural Gas (LNG) in production, storage, loading/unloading, transportation/shipping, and re-gasification have became a major concern, since an accident in the LNG industry would be very costly. Understanding the threat of LNG not only contributes to the process safety and reliability in the research and development (R&D) system, but improves the efficiency of loss prevention, fire protection and emergency responses. As of April 2019, in order to obtain the present status and trend of LNG safety research, basing 1122 documents of the Web of Science database about safety research of LNG as a data source, CiteSpace and VOS viewer were used for network knowledge map analysis. A comprehensive knowledge map of LNG safety field was obtained from several research aspects including scientific research power, research hot spots and trends, research knowledge base and frontier. According to the study results, the development of LNG safety research was divided into four stages from 1970s to 2019, China and South Korea made a lot of contributions, and the United States is the most influential. Among them, the research from 2005 to 2019 was the most representative. Current research results indicate that a combination of Formal Safety Assessment (FSA) methodology and Dynamic Procedure for Atypical Scenarios Identification (DyPASI) will fully identify risks; The PHAST and TerEx programs quickly define safety zones. Computational Fluid Dynamics (CFD) software package can provide accurate quantitative data for the study of LNG safety. Research on quantitative risk assessment (QRA) and LNG evaporated gas (BOG) has been a hot topic and trend in this field. The application of expansion foam in LNG accident mitigation covers most of the research content in this field, and the optimization of LNG liquefaction process has a great influence on this industry. As the international demand for LNG energy output increases, floating liquefied natural gas (FLNG) will have considerable development, and increasingly researchers attach vital importance to the safety of LNG offshore production integrated unit.     

Research on the prediction model of hazardous chemical road transportation accidents

In recent years, hazardous chemicals road transport accidents have occurred frequently, causing huge casualties and property losses, and accident risk assessment has become the focus of researchers' research. To predict the risk probability value of hazardous chemical road transport accidents, first, we compiled data on road transportation accidents of hazardous chemicals in China in the past five years. And the nine nodes in the Bayesian network (BN) structure were defined in combination with relevant classification standards. The optimal Bayesian network structure for hazardous chemical road transport accidents was determined based on the K2 algorithm and the causalities between the nodes. Second, the node conditional probabilities were derived by parameter learning of the model using Netica, and the validity of the model was verified using the 5-fold cross-validation method. Last, the Bayesian network model of hazardous chemical road transport accidents is used to analyze accident examples, and the accident chain of “rear-end-leakage” is predicted, and the accident is most likely to be disposed of within 3–9 h. The study shows that the derived accident prediction model for hazardous chemical road transportation can reason reasonably about the evolution of accident scenarios and determine the probability values of accident risks under different parameter conditions.     

基于改进贝叶斯网络的加油站火灾爆炸分析

为了找出导致加油站发生火灾爆炸事故的基本事件及其可能性大小,以加油站火灾爆炸故障树为基础建立相应的贝叶斯网络风险模型。在FTA向BN转化算法的基础上对条件概率做出了修正。利用GeNIe软件计算加油站火灾爆炸事故基事件的后验概率,同时进行灵敏度和影响力分析。最后通过实例分析,找出了导致某加油站发生火灾爆炸事故危险性最大的因素集为:加油站接打手机、机械碰撞、给塑料容器加油、加油冒油、油枪渗漏等。结果表明,注重基事件的多态性和事件间逻辑关系合理性的新模型,能推算出更准确的基事件概率分布,同时可以找出导致事故发生的最有可能途径,为加油站事故预防,系统改进提供较为合理性建议。     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.     

基于故障树的生物质气化中毒事故BN-LOPA分析

为降低生物质气化中毒事故的概率,本文提出基于故障树的贝叶斯—保护层复合分析法(BN-LOPA),对生物质气化中毒事故进行风险分析。以辽宁省某气化站为例,通过贝叶斯网络计算出该气化站中毒事故发生概率为1.11×10^-4,基于贝叶斯网络推导出中毒事故基本事件的后验概率,得到导致事故发生的故障节点,并对其设置独立防护层,经推导计算,该气化站的气化中毒事故发生概率可降低至5.35×10^-6,风险等级从7级降至5级。结果表明:该气化站增加独立防护层后,符合安全生产的要求。     

基于HCNAM-BN的化工过程爆炸事故致因链分析

为了从源头上预防化工过程爆炸事故,依据风险耦合理论,探讨了各风险因子非线性耦合演化为爆炸事故的机理,构建了层次耦合网络分析模型（HCNAM）;从多因素风险耦合角度分析了国内外44起典型化工过程爆炸事故,统计了各风险因子之间的耦合概率并进行了耦合致因重要度分级;采用耦合概率与二态分布相结合的条件概率分布,将层次耦合网络分析模型转化为贝叶斯网络,并对氯乙烯单体槽爆炸性混合气体爆炸事故进行了应用研究。结果表明:91种双因子耦合风险状态中,47种呈现弱耦合致因特性;7种因子双耦合形成风险的概率较大;基于HCNAM-BN模型分析事故,可有效辨识事故最可能致因因素,获取各事故致因链的发生概率并确定事故网络关键节点。     

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。     

基于二维灰云模型的LNG动力船航行过程风险推理*

针对风险多维属性问题及船舶航行多种风险表现,提出基于二维灰云模型的LNG动力船航行过程风险推理方法。针对LNG使用及船舶航行2种作业方式,采用系统方法识别安全风险影响因素体系,确定2种作业方式下各风险体系指标变化权重,描述LNG动力船航行过程中LNG使用和船舶航行发生事故概率及后果;通过引入云模型,综合计算2种作业方式下概率及后果组合风险,并采用灰云推理2种作业方式同时存在时LNG动力船航行过程风险。结果表明:LNG动力船航行过程中LNG使用和船舶航行风险耦合,风险程度受控;二维灰云模型可有效分析多维属性下风险评估问题,以及不同作业下风险耦合作用。研究结果可为船舶航行过程风险定量评估提供依据。     

化肥厂爆炸事故的不安全动作分析

为了分析化肥厂爆炸事故背后的不安全动作。本文选取了2009-2018年间国内外发生的52起典型化肥厂爆炸事故。从发生时间的角度分析事故特性,发现白昼期间的事故发生概率相对较高。同时文章分析了运输卸料、车间操作、特种作业和管理人员4种作业类型的不安全动作,从发生频率和风险等级两个方面评估了不安全动作的综合危险度。结果表明13项不安全动作的综合危险度较高,需要重点预控。     

Quantitative risk analysis of urban natural gas pipeline networks using geographical information systems

This paper presents a novel quantitative risk analysis process for urban natural gas pipeline networks using geographical information systems (GIS). The process incorporates an assessment of failure rates of integrated pipeline networks, a quantitative analysis model of accident consequences, and assessments of individual and societal risks. Firstly, the failure rates of the pipeline network are calculated using empirical formulas influenced by parameters such as external interference, corrosion, construction defects, and ground movements. Secondly, the impacts of accidents due to gas leakage, diffusion, fires, and explosions are analyzed by calculating the area influenced by poisoning, burns, and deaths. Lastly, based on the previous analyses, individual risks and social risks are calculated. The application of GIS technology helps strengthen the quantitative risk analysis (QRA) model and allows construction of a QRA system for urban gas pipeline networks that can aid pipeline management staff in demarcating high risk areas requiring more frequent inspections.     

Real-time risk assessment of explosion on offshore platform using Bayesian network and CFD

Combustion or explosion accident resulting from accidental hydrocarbon release poses a severe threat to the offshore platform's operational safety. Much attention has been paid to the risk of an accident occurring over a long period, while the real-time risk that escalates from a primary accident to a serious one was ignored. In this study, a real-time risk assessment model is presented for risk analysis of release accidents, which may escalate into a combustion or explosion. The proposed model takes advantage of Fault Tree-Event Tree (FT-ET) to describe the accident scenario, and Bayesian network (BN) to obtain the initial probability of each consequence and describe the dependencies among safety barriers. Besides, Computational Fluid Dynamics (CFD) is applied to handle the relationship between gas dispersion and time-dependent risk. Ignition probability model that considering potential ignition sources, gas cloud, and time series are also integrated into this framework to explain the likelihood of accident evolution. A case of release accidents on a production platform is used to test the availability and effectiveness of the proposed methodology, which can be adopted for facilities layout optimization and ignition sources control.     

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.     

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.     

风险因子模糊评价法在钻机安全评价中的应用

钻机是石油与天然气钻井作业的重要设备,在使用过程中存在着极大风险,如果对其中的风险因素管理不善,风险转化为事故,将造成极大的损失,严重影响企业的形象与生产。为了预防石油与天然气钻井作业过程的事故发生,提高钻井队安全管理水平,论文采用通过对系统技术风险概率Pf和事故后果的严重程度Cf的估计和模糊评判的方法得到系统的风险状况Rf,为采取科学合理的风险消减措施提供了决策依据。论文以某型号电动钻机的电气控制系统为例进行风险分析,分析结果表明与实际情况比较符合。     

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.