Dynamic risk assessment using failure assessment and Bayesian theory

To ensure the safety of a process system, engineers use different methods to identify the potential hazards that may cause severe consequences. One of the most popular methods used is quantitative risk assessment (QRA) which quantifies the risk associated with a particular process activity. One of QRA's major disadvantages is its inability to update risk during the life of a process. As the process operates, abnormal events will result in incidents and near misses. These events are often called accident precursors. A conventional QRA process is unable to use the accident precursor information to revise the risk profile. To overcome this, a methodology has been proposed based on the work of Meel and Seider (2006). Similar to Meel and Seider (2006) work, this methodology uses Bayesian theory to update the likelihood of the event occurrence and also failure probability of the safety system. In this paper the proposed methodology is outlined and its application is demonstrated using a simple case study. First, potential accident scenarios are identified and represented in terms of an event tree, next, using the event tree and available failure data end-state probabilities are estimated. Subsequently, using the available accident precursor data, safety system failure likelihood and event tree end-state probabilities are revised. The methodology has been simulated using deterministic (point value) as well as probabilistic approach. This Methodology is applied to a case study demonstrating a storage tank containing highly hazardous chemicals. The comparison between conventional QRA and the results from dynamic failure assessment approach shows the significant deviation in system failure frequency throughout the life time of the process unit.     

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.     

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.     

Gas dispersion risk analysis of safety gap effect on the innovating FLNG vessel with a cylindrical platform

While the effect of the safety gap on explosions is well known, little has been carried out to evaluate the effect of the safety gap on dispersion of gas releases, this paper evaluates the effect of safety gap on gas dispersion for a cylindrical Floating Liquefied Natural Gas (FLNG) vessel. The realistic ship-shaped and circular FLNG platforms are established and used for the detailed CFD based analysis; rather than the structural and hydrodynamics advantages of mobility, stability and cost efficiency etc., this study aims to investigate the safety of gas dispersion on the cylindrical FLNG and compare the safety gap effects on different configurations. A series of different safety gap configurations are evaluated for gas dispersion occurring in near field for the traditional FLNG while both near field and far field gas dispersion simulations are conducted on the cylindrical one. The overall results indicate that the safety gap is effective in reducing the gas cloud size in both FLNG configurations, however, when it comes to the gas dispersion in the far field against the leakage point, the safety gap increases the gas cloud size in the cylindrical FLNG vessel on the contrary.     

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.     

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.     

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.     

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。     

“三高”气田钻井过程硫化氢泄漏动态风险分析

传统的H2S泄漏风险分析方法不能很好地对事故发展过程进行动态分析,导致分析结果偏离实际。基于贝叶斯方法,构建了高温、高压、高含硫（“三高”）气田钻井过程中H2S泄漏的蝴蝶结模型并提出将其转化为贝叶斯网络,在事故已发生的情况下更新基本事件发生的概率。然后,假定事故后果在确定的时间段内发生的累积次数已知的条件下,更新安全屏障及事故后果发生的概率,从而完成对H2S泄漏的动态风险分析。结果表明,该方法克服了传统静态定量分析方法中的不足,可动态评估导致H2S泄漏的基本事件发生的概率和对顶事件发生的影响程度,并动态反映安全屏障和事故后果的风险变化,能为钻井过程中H2S泄漏的风险分析及防控措施提供参考。     

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.     

Application of Bayesian Regularization Artificial Neural Network in explosion risk analysis of fixed offshore platform

Computational Fluid Dynamics (CFD) is routinely used in Explosion Risk Analysis (ERA), as CFD-based ERA offers a good understanding of underlying physics accidental loads. Generally, simplifications were incorporated into CFD-based ERA to limit the number of simulations. Frozen Cloud Approach (FCA) is a frequently used simplification in the dispersion part of the CFD-based ERA procedure. However, its accuracy is questionable in the complex and congested environment such as offshore facility. Furthermore, in explosion part, some specific techniques, e.g. linear/double bin-interpolated techniques have been proposed while the corresponding accuracy is still unknown since the developers did not yet check their accuracy by considering the explosion computational data as the benchmark.This study presents a more accurate algorithm, namely Bayesian Regularization Artificial Neural Network (BRANN) and accordingly proposes the frameworks regarding BRANN-based models for the CFD-based ERA procedure. Firstly, the framework is proposed to develop the Transient-BRANN (TBRANN) model for transient dispersion study. In addition, the framework to determine the BRANN model for explosion study is developed. The proposed frameworks are explained by a case study of the fixed offshore platform. Consequently, this study confirms the more accuracy of the TBRANN model over FCA and the accuracy of BRANN model for CFD-based ERA.     

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...     

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.     

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.     

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.     

Fire and risk analysis during loading and unloading operation in liquefied petroleum gas (LPG) bottling plant

The article focuses on analyzing risks associated with the gas transfer operation in a liquified petroleum gas (LPG) bottling plant in India. The transfer operations involve transferring liquified gas from the transport tanker to the underground storage tank. Due to the rapid expansion of the cities, many LPG bottling plants in India got surrounded by residential areas and business centers. Moreover, to maintain the supply chain, the frequency of the transfer operations at the bottling plant also increased. In this scenario, an accidental release of LPG during the transfer operation may lead to various consequences such as a pool fire, a fireball, and even a catastrophic rupture of the tank with a successive explosion of its contents. In the study, the operations involved in bottling plants are classified into different hazard zones and analyzed. The probability of occurrence of events leading to an accident is modeled using modeling tools such as ALOHA and PHAST. The consequences of an accident following various events, such as jet fire, fireball, etc., are modeled, and the simulation results are compared. The thermal radiation has been estimated as 4–40 kW/m², which could adversely affect the nearby population and could result in damaging plant machinery and equipment.     

Methods and models in process safety and risk management: Past,present and future

The paper reviews past progress in the development of methods and models for process safety and risk management and highlights the present research trends; also it outlines the opinions of the authors regarding the future research direction in the field. Based on the open literature published in the leading journals in the field of safety, risk and reliability, the review covers the evolution of the methods and models developed for process safety and risk management. The methods and models are categorized as qualitative, semi-quantitative, quantitative and hybrid. The progress in the last few decades is discussed in the context of the past. Developments in the current decade formulate the basis of the present trends; future directions for research in these fields are also outlined. The aim of the article is to provide a historical development in this field with respect to the driving forces behind the development. It is expected that it will help researchers and industrial practitioners to gain a better understanding of the existing concepts. At the same time the aim is to provide direction to bridge the existing gaps through research and developments.     

Individual risk analysis of high-pressure natural gas pipelines

Transmission pipelines carrying natural gas are not typically within secure industrial sites, but are routed across land out of the ownership of the pipeline company. If the natural gas is accidentally released and ignited, the hazard distance associated with these pipelines to people and property is known to range from under 20 m for a smaller pipeline at lower pressure to up to over 300 m for a larger pipeline at higher pressure. Therefore, pipeline operators and regulators must address the associated public safety issues.This paper focuses on a method to explicitly calculate the individual risk of a transmission pipeline carrying natural gas. The method is based on reasonable accident scenarios for route planning related to the pipeline's proximity to the surrounding buildings. The minimum proximity distances between the pipeline and buildings are based on the rupture of the pipeline, with the distances chosen to correspond to a radiation level of approximately 32 kW/m². In the design criteria for steel pipelines for high-pressure gas transmission (IGE/TD/1), the minimum building proximity distances for rural areas are located between individual risk values of 10⁻⁵ and 10⁻⁶. Therefore, the risk from a natural gas transmission pipeline is low compared with risk at the building separated minimum distance from chemical industries.     

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.     

基于逻辑树和贝叶斯网络的海洋平台火灾概率分析

火灾概率分析是海洋平台火灾定量风险评估的重要组成部分,考虑到传统事故树和事件树方法存在一定的局限性,提出了基于逻辑树和贝叶斯网络的火灾概率分析模型。首先采用数理统计方法对墨西哥湾地区2 837起火灾事故进行统计分析,依据事故情况构建逻辑树,然后将逻辑树转化为贝叶斯网络,根据历史数据确定贝叶斯网络各节点的先验概率和条件概率。结果表明:海洋平台火灾事故是设备、人因和组织管理多因素耦合作用的结果;基于贝叶斯网络模型得到海洋平台火灾概率约为1.0×10~(-5),为海洋平台火灾定量风险评估提供了基础数据;由贝叶斯网络模型分析得出,人因操作失误与缺乏作业安全分析的后验概率分别达到0.471和0.119,表明人因组织因素对海洋平台火灾事故具有重要影响。