On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

Since gas plants are progressively increasing near urban areas, a comprehensive tool to plan maintenance and reduce the risk arising from their operations is required. To this end, a comparison of three Risk-Based Maintenance methodologies able to point out maintenance priorities for the most critical components, is presented in this paper. Moreover, while the literature is mostly focused on probabilistic analysis, a particular attention is directed towards consequence analysis throughout this study. The first developed technique is characterized by a Hierarchical Bayesian Network to perform the occurrence analysis and a Failure Modes, Effects and Criticality Analysis to assess the magnitude of the adverse outcomes. The second approach is a Quantitative Risk Analysis carried out via a software named Safeti. Finally, another software called Synergi Plant is adopted for the third methodology, which provides a Risk-Based Inspection plan, through a semiquantitative risk analysis. The proposed study can assist asset manager in adopting the most appropriate methodology to their context, while highlighting priority components. To demonstrate the applicability of the approaches and compare their rankings, a Natural Gas Regulating and Measuring Station is considered as case study. The results showed that the most suited method strongly depends on the available data.     

基于FBN地铁深基坑施工渗漏风险评估模型及应用

为了解决由于地铁深基坑施工风险因素不确定性和基坑工程事故资料缺失导致传统风险分析方法不再满足实际需要的问题。探讨模糊集理论(FST)和贝叶斯网络(BN)的结合,介绍1种专家置信度指标,建立地铁深基坑渗漏风险评估指标体系,得到基于模糊贝叶斯网络(FBN)地铁深基坑施工渗漏风险评估模型。研究结果表明：将该方法应用于广州地铁十三号线某车站深基坑施工渗漏风险评估中,结果符合施工实际,接缝密封质量差等风险因素需加以措施控制,该方法可为后续施工风险评估提供实时支持。     

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.     

Technical,human, and organizational factors affecting failures of firefighting systems (FSs) of atmospheric storage tanks: Providing a risk assessment approach using Fuzzy Bayesian Network (FBN) and content validity indicators

Most risk assessment methods have problems such as uncertainty, static structure, and lack of validation. Also, in most of these studies, less attention has been paid to human, managerial, and organizational issues. Therefore, this study proposes a risk assessment method based on the Fuzzy Bayesian Network (FBN) to prevent failure of firefighting systems (FFSs) in the atmospheric Storage Tanks of a Petrochemical Industry. The first stage of the study is the development of a fault tree (FT) and investigation of basic events (BEs). In this study, content validity indices and brainstorming technique were used to validate the FT structure and reduce the uncertainties of Completeness, Modeling, and Parameter. After determining the probability of basic events (BEs) by the expert team opinions and fuzzy logic, events were transmitted to the Bayesian Network (BN) and then analyzed with deductive and inductive reasoning, followed by sensitivity analysis in the GeNIe software. Finally, results of a case study in the Atmospheric Storage Tanks of the Methanol Floating Roof of a Petrochemical Industry showed that FBN simulation and FT validation could provide a practical way to determine FFSs probabilities, identify impactful events, and reduce the above uncertainties. Also, taking account of hidden factors of events, such as organizational and managerial factors, can help managers to prevent FFSs in tanks.     

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.     

“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。     

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.     

A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors

INTRODUCTION: Focusing on people and organizations, this paper aims to contribute to offshore safety assessment by proposing a methodology to model causal relationships. METHOD: The methodology is proposed in a general sense that it will be capable of accommodating modeling of multiple risk factors considered in offshore operations and will have the ability to deal with different types of data that may come from different resources. Reason's "Swiss cheese" model is used to form a generic offshore safety assessment framework, and Bayesian Network (BN) is tailored to fit into the framework to construct a causal relationship model. The proposed framework uses a five-level-structure model to address latent failures within the causal sequence of events. The five levels include Root causes level, Trigger events level, Incidents level, Accidents level, and Consequences level. To analyze and model a specified offshore installation safety, a BN model was established following the guideline of the proposed five-level framework. A range of events was specified, and the related prior and conditional probabilities regarding the BN model were assigned based on the inherent characteristics of each event. RESULTS: This paper shows that Reason's "Swiss cheese" model and BN can be jointly used in offshore safety assessment. On the one hand, the five-level conceptual model is enhanced by BNs that are capable of providing graphical demonstration of inter-relationships as well as calculating numerical values of occurrence likelihood for each failure event. Bayesian inference mechanism also makes it possible to monitor how a safety situation changes when information flow travel forwards and backwards within the networks. On the other hand, BN modeling relies heavily on experts' personal experiences and is therefore highly domain specific. IMPACT ON INDUSTRY: "Swiss cheese" model is such a theoretic framework that it is based on solid behavioral theory and therefore can be used to provide industry with a roadmap for BN modeling and implications. A case study of the collision risk between a Floating Production, Storage and Offloading (FPSO) unit and authorized vessels caused by human and organizational factors (HOFs) during operations is used to illustrate an industrial application of the proposed methodology.     

An extension to Fuzzy Fault Tree Analysis (FFTA) application in petrochemical process industry

Fault Tree Analysis (FTA) is an established technique in risk management associated with identified hazards specific to focused fields. It is a comprehensive, structured and logical analysis method aimed at identifying and assessing hazards of complex systems. To conduct a quantitative FTA, it is essential to have sufficient data. By considering the fact that sufficient data is not always available, the FTA method can be adopted into the problems under fuzzy environment, so called as Fuzzy Fault Tree Analysis (FFTA). This research extends FFTA methodology to petrochemical process industry in which fire, explosion and toxic gas releases are recognized as potential hazards. Specifically, the case study focuses on Deethanizer failure in petrochemical plant operations to demonstrate the proposed methodology. Consequently, the study has provided theoretical and practical values to challenge with operational data shortage in risk assessment.     

基于BN的FTA在通用航空风险评价中的应用

针对事故树分析法(FTA)在风险评价中的局限性,采用以事故树为基础建立的贝叶斯网络(BN)风险模型,对通用航空中的两机空中相撞事故进行分析和推理,对事故模型进行改进和修正时,注重基事件的多态性和事件间的逻辑合理性。根据贝叶斯推理得出的数据,找到了事故的主要致因。结果表明,基于BN的FTA既能向前预测顶事件的发生概率,又能向后诊断基本事件的后验概率,可以更好地对通用航空风险进行评价。     

Interactions of risk analysis and contingency planning in risk management

Risk management entails knowledge of the risk and how best to reduce it; its objective is to minimize losses arising from existing or potential risk. With effective contingency planning, risk analysis and its corollary, consequence analysis, can contribute synergistically to improved risk management. Until recently, risk analysis and contingency or emergency response planning were considered distinct disciplines with little interactive potential. Fortunately, industry now recognizes that linking the two can help ensure public safety as well as preserve the financial integrity of plant owners. Both areas are receiving increased and well-deserved attention; several incidents in recent years have demonstrated that losses could have been greatly reduced if better precautions and procedures had been in place as a result of risk analysis and contingency planning.     

Modelling of BP Texas City refinery accident using dynamic risk assessment approach

Process industries involve handling of hazardous substances which on release may potentially cause catastrophic consequences in terms of assets lost, human fatalities or injuries and loss of public confidence of the company. In spite of using endless end-of-the-pipe safety systems, tragic accidents such as BP Texas City refinery still occur. One of the main reasons of such rare but catastrophic events is lack of effective monitoring and modelling approaches that provide early warnings and help to prevent such event. To develop a predictive model one has to rely on past occurrence data, as such events are rare, enough data are usually not available to better understand and model such behavior. In such situations, it is advisable to use near misses and incident data to predict system performance and estimate accident likelihood. This paper is an attempt to demonstrate testing and validation of one such approach, dynamic risk assessment, using data from the BP Texas City refinery incident.Dynamic risk assessment is a novel approach which integrates Bayesian failure updating mechanism with the consequence assessment. The implementation of this methodology to the BP Texas City incident proves that the approach has the ability to learn from near misses, incident, past accidents and predict event occurrence likelihood in the next time interval.     

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RPN-BN的平台-隔水管系统遇内波风险分析

为保障内孤立波作用下的深水半潜式钻井平台-隔水管系统的安全,同时解决海洋平台系统设备失效数据的缺失问题,提出1种风险优先系数（RPN）与贝叶斯（BN）结合的定量风险分析方法。首先,基于故障树和安全屏障方法,建立平台-隔水管系统Bow tie模型和贝叶斯风险演化模型;其次,根据贝叶斯推断和风险优先系数中的事故发生频度估计,得到平台-隔水管系统失效事故的发生概率;最后,通过贝叶斯网络的逆向推理能力辨识内孤立波作用下引起平台-隔水管系统失效的主要风险节点,实现对平台-隔水管系统失效事故的定量风险分析。结果表明:RPN-BN法可应用于平台-隔水管系统遇内波的定量风险分析;加强对平台漂移量的控制,提高动力定位系统控制设备的可靠性可有效抵御内波对系统造成的影响。     

Methodology for global sensitivity analysis of consequence models

A methodology is presented for global sensitivity analysis of consequence models used in process safety applications. It involves running a consequence model around a hundred times and using the results to construct a statistical emulator, which is essentially a sophisticated curve fit to the data. The emulator is then used to undertake the sensitivity analysis and identify which input parameters (e.g. operating temperature and pressure, wind speed) have a significant effect on the chosen output (e.g. vapour cloud size). Performing the sensitivity analysis using the emulator rather than the consequence model itself leads to significant savings in computing time.To demonstrate the methodology, a global sensitivity analysis is performed on the Phast consequence model for discharge and dispersion. The scenarios studied consist of above-ground, horizontal, steady-state discharges of dense-phase carbon dioxide (CO₂), with orifices ranging in diameter from ½ to 2 inch and the liquid CO₂ stagnation conditions maintained at between 100 and 150 bar. These scenarios are relevant in scale to leaks from large diameter above-ground pipes or vessels.Seven model input parameters are varied: the vessel temperature and pressure, orifice size, wind speed, humidity, ground surface roughness and height of the release. The input parameters that have a dominant effect on the dispersion distance of the CO₂ cloud are identified, both in terms of their direct effect on the dispersion distance and their indirect effect, through interactions with other varying input parameters.The analysis, including the Phast simulations, runs on a standard office laptop computer in less than 30 min. Tests are performed to confirm that a hundred Phast runs are sufficient to produce an emulator with an acceptable degree of accuracy. Increasing the number of Phast runs is shown to have no effect on the conclusions of the sensitivity analysis.The study demonstrates that Bayesian analysis of model sensitivity can be conducted rapidly and easily on consequence models such as Phast. There is the potential for this to become a routine part of consequence modelling.     

Fuzzy risk modeling of process operations in the oil and gas refineries

Operating several assets has resulted in more complexity and so occurrence of some major accidents in the refining industries. The process operations risk factors including failure frequency and the consequence components like employees' safety and environment impacts, operation downtime, direct and indirect cost of operations and maintenance, and mean time to repair should be considered in the analysis of these major accidents in any refinery. Considering all of these factors, the risk based maintenance (RBM) as a proper risk assessment methodology minimizes the risk resulting from asset failures. But, one of the main engineering problems in risk modeling of the complex industries like refineries is uncertainty due to the lack of information. This paper proposes a model for the risk of the process operations in the oil and gas refineries. The fuzzy logic system (FLS) was proposed for risk modeling. The merit of using fuzzy model is to overcome the uncertainty of the RBM components. This approach also can be accounted as a benchmark for future failures. A unified risk number would be obtained to show how the criticality of units is. The case study of a gas plant in an oil refinery is performed to illustrate the application of the proposed model and a comparison between the results of both traditional RBM and fuzzy method is made.For the case study, 26 asset failures were identified. The fuzzy risk results show that 3 failures have semi-critical level and other 23 failures are non-critical. In both traditional and fuzzy RBM methods, some condenser failures had the highest risk number and some pumps were prioritized to have the lowest risk level. The unit with unified risk number less than 40 is in the non-critical conditions. Proposed methodology is also applicable to other industries dealing with process operations risks.     

基于贝叶斯网络的机械安全性评估模型研究

重点阐述了基于贝叶斯网络的机械安全性评估模型的建立过程,应用贝叶斯网络建立了开式压力机安全性评估模型,对冲手事故模型中的共因失效节点进行了说明,并最终计算得出开式压力机冲手事故的发生概率.计算结果分析表明:对于系统基本事件之间有共因和相关关系的系统,BN计算结果使得事故后果概率明显增大,设计者和管理者不能忽略共因和相关关系对系统风险的影响.     

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。     

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.     

Critical aspects for collision induced oil spill response and recovery system in ice conditions: A model-based analysis

The recovery effectiveness for oil spills in ice conditions depends on a complex system and has not been studied in depth, especially not from a system risk control perspective. This paper aims to identify the critical aspects in the oil spill system to enable effective oil spill recovery. First, a method is developed to identify critical elements in a Bayesian Network model, based on an uncertainty-based risk perspective. The method accounts for sensitivity and the strength of evidence, which are assessed for the different Bayesian Network model features. Then, a Bayesian Network model for the mechanical oil spill recovery system is developed for the Finnish oil spill response fleet, contextualized for representative collision accident scenarios. This model combines information about representative sea ice conditions, ship-ship collisions and their associated oil outflow, the oil dispersion and spreading in the ice conditions, and the oil spill response and recovery of the fleet. Finally, the critical factors are identified by applying the proposed method to the developed oil spill response system model. The identified most critical system factors relates collision aspect: Forcing Representative Scenario, Representative Accident Location, Impact Speed, Impact Location, Impact Angle and response aspect: Response Vessel Operability.