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.     

Safety study of an LNG regasification plant using an FMECA and HAZOP integrated methodology

A safety analysis was performed to determine possible accidental events in the storage system used in the liquefied natural gas regasification plant using the integrated application of failure modes, effects and criticality analysis (FMECA) and hazard and operability analysis (HAZOP) methodologies. The goal of the FMECA technique is the estimation of component failure modes and their major effects, whereas HAZOP is a structured and systematic technique that provides an identification of the hazards and the operability problems using logical sequences of cause-deviation-consequence of process parameters. The proposed FMECA and HAZOP integrated analysis (FHIA) has been designed as a tool for the development of specific criteria for reliability and risk data organisation and to gain more recommendations than those typically provided by the application of a single methodology. This approach has been applied to the risk analysis of the LNG storage systems under construction in Porto Empedocle, Italy. The results showed that FHIA is a useful technique to better and more consistently identify the potential sources of human errors, causal factors in faults, multiple or common cause failures and correlation of cause-consequence of hazards during the various steps of the process.     

Factors in offshore planning that affect the risk for major accidents

The purpose of this paper is to systematically analyse a typical planning process in the offshore industry from the perspective of causes of major accidents, with the ultimate aim of identifying factors that affect the risk for major accidents occurring. We first study and describe a typical planning process for offshore oil and gas operations in Norway. Then we analyse a number of theories of major accidents, to see how the different theories and their explanations of causes and contributing factors can be of relevance for future plans and planning processes. Finally, we review accident investigations to search for evidence of how weaknesses in planning processes can contribute to major accidents through the above identified factors. Also, we try to identify any additional factors that have not been recognised through the theoretical review. This provides empirical support for the theoretical basis. Thirteen factors which directly or indirectly can influence the planning process causing a major accident potential are identified. These are exemplified through a review of investigation reports. The paper suggests that planning process should focus more on increasing quality in the plans at an early phase, with examples from incidents, and illustrate the relation between planning quality and potential for major accidents.     

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.     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

Fault detection and diagnostics of a three-phase separator

A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator.     

Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model

A substantial amount of oil & gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. In British Columbia (BC), Canada, alone there are over 40,000 km of pipelines currently being operated. Because of the adverse environmental impact, public outrage and significant financial losses, the integrity of the pipelines is essential. More than 37 pipe failures per year occur in BC causing liquid spills and gas releases, damaging both property and environment. BC oil & gas commission (BCOGS) has indicated metal loss due to internal corrosion as one of the primary causes of these failures. Therefore, it is of a paramount importance to timely identify pipelines subjected to severe internal corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this paper presents a Bayesian belief network (BBN)-based probabilistic internal corrosion hazard assessment approach for oil & gas pipelines. A cause-effect BBN model has been developed by considering various information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF). This paper also explores the influence of fluid composition and operating conditions on the corrosion rate and PoF. To demonstrate the application of the BBN model, a case study of the Northeastern BC oil & gas pipeline infrastructure is presented. Based on the pipeline's mechanical characteristics and operating conditions, spatial and probabilistic distributions of corrosion defect and PoF have been obtained and visualized with the aid of the Geographic Information System (GIS). The developed BBN model can identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision-making process.     

油气长输管道作业分级定量风险评价方法研究及应用

为客观掌握油气管道生产运营岗位主要作业风险,实现油气长输管道企业作业风险管理,提出一种作业分级定量风险评价方法。首先,辨识油气管道系统主要危险作业类型和引起作业事故的危险有害因素;然后由作业分级法确定事故发生的可能性与严重性;最后建立基于作业分级的风险矩阵,得到各作业活动的风险级别。将该方法应用于某管道公司,以该公司生产区内部高处作业为例,对作业过程中的危险有害因素进行识别与评价,得出高处作业过程中各步骤的风险程度。结果表明该评价方法具有一定的可操作性,可反映各类危险作业过程中的风险程度,为后期有针对性的风险控制提供指导,有助于公司的风险管理。     

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。     

A structural approach to the HAZOP – Hazard and operability technique in the biopharmaceutical industry

Recently, the use of analytical techniques to identify, assess and address risks within the pharmaceutical industry is increasing from the initial and operating phases until the final use of products aiming to eliminate or reduce the severity of deviations. The hazard and operability studies – HAZOP establish that accidents are the result of failure modes in process variables out of operational parameters. In this paper, the HAZOP methodology was used to assess risks in the system for recombinant protein production where a multidisciplinary group used the brainstorming strategy to identify the risk level and deviations in nodes defined by functionality in the system. Nineteen critical nodes were identified, deviations were established in based on knowledge, and experience by the group, thus precluded the need of deviation's records to estimate frequency and impacts of events. It was also shown that in the pharmaceutical industry the most-critical risks are those that have adverse impacts on production like partial and total losses and when noncompliance of regulations are involved. The HAZOP risk assessment tool can be easily followed by people who are interested in starting to use this technique to improve the security environment within the institution and when required by regulatory agencies.     

HAZOP分析方法在石油工业上游业务中的应用

基于国内石油工业上游业务风险分析和安全评价现状,针对油气勘探、油气田开发、油气集输和海洋石油各阶段工艺流程与作业环境特性,选择引导词、偏差进行安全评价,分析和探讨HAZOP分析方法在上游业务中应用的可行性和适用性。研究表明,对于油气田开发、油气集输阶段以及海上油气田生产方面,应用HAZOP分析,可从本质安全角度提出项目风险管理控制措施,提高装置安全性和可操作性,促进企业持续稳定发展。但对于油气勘探阶段,由于其风险呈层次型且评价方法依赖于数学建模和数值计算,不适宜采用HAZOP分析方法。     

Dynamic failure assessment of an ammonia storage unit: A case study

Chemical Process Industries usually contain a diverse inventory of hazardous chemicals and complex systems required to perform process operations such as storage, separation, reaction, compression etc. The complex interactions between the equipment make them vulnerable to catastrophic accidents. Risk and failure assessment provide engineers with an intuitive tool for decision making in the operation of such plants. Abnormal events and near-miss situations occur regularly during the operation of a system. Accident Sequence Precursors (ASP) can be used to demonstrate the real-time operating condition of a plant. Dynamic Failure Assessment (DFA) methodology is based on Bayesian statistical methods incorporates ASP data to revise the generic failure probabilities of the systems during its operational lifetime.In this paper, DFA methodology is applied on an ammonia storage unit in a specialized chemical industry. Ammonia is stored in cold storage tanks as liquefied gas at atmospheric pressure. These tanks are susceptible to failures due to various abnormal conditions arising due process failures.Tank failures due to three such abnormal conditions are considered. Variation of the failure probability of the safety systems is demonstrated. The authors use ASP data collected from plant specific sources and safety expert judgement. The failure probabilities of some safety systems concerned show considerable deviation from the generic values. The method helps to locate the components which have undergone more degradation over the period and hence must be paid attention to. In addition, a Bayesian predictive model has been used to predict the number of abnormal events in the next time interval. The user-friendly and intuitive nature of the tool makes it appropriate for application in safety assessment reports in process industries.     

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.     

An integrated quantitative risk analysis method for natural gas pipeline network

Natural gas industry is developing rapidly, and its accidents are threatening the urban safety. Risk management through quantitative assessment has become an important way to improve the safety performance of the natural gas supply system. In this paper, an integrated quantitative risk analysis method for natural gas pipeline network is proposed. This method is composed of the probability assessment of accidents, the analysis of consequences and the evaluation of risk. It is noteworthy that the consequences analyzed here include those of the outside and inside gas pipelines. The analysis of consequences of the outside pipelines focuses on the individual risk and societal risk caused by different accidents, while those of the inside pipelines concerns about the risk of the economic loss because of the pressure re-distribution. Risk of a sample urban gas pipeline network is analyzed to demonstrate the presented method. The results show that this presented integrated quantitative risk analysis method for natural gas pipeline network can be used in practical application.     

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.     

基于改进LEC法的危化企业静电点燃危险评价

危化企业爆炸性危险环境下由静电放电引发的火灾爆炸时有发生,针对这一问题分析了危化企业气体、液体、固体、粉体及人体在不同生产工艺过程中静电电荷来源以及可能的静电放电形式。综合考虑静电点燃源形成可能性、爆炸性环境形成可能性、监控与控制措施有效性以及静电事故后果严重度,构建了基于改进LEC法的静电点燃危险评价方法,该评价方法能实现对危化企业静电点燃源危害的量化评估与分级。应用该方法对某加油站进行静电点燃危险评价,并根据评价结果提出了预防改进措施。     

油气管道风险分析的质量评价研究

从风险分析的不确定性入手,建立了管道风险分析的质量评价观点.基于油气管道风险分析的实施步骤,提出用检查表法对风险分析的完整过程进行质量评价,利用风险评价员的一致性和有效性指标对其评价结果进行质量优劣评定.鉴于油气管道风险分析中普遍采用评分指标法对事故发生概率进行估计,依据采取这种行为所要承担损失风险的大小程度,提出用风险函数法对这种半定量评价法的合理性进行质量评价.实例应用分析表明,本文提出的质量评价方法不仅可以辨识风险分析过程中的人因缺陷,而且可对油气管道风险分析结果的质量进行量化评价,有利于提高人们对风险分析结果的认同度.     

Offshore oil and gas occupational accidents—What is important?

Occupational accidents continue to present a problem to industry in general and the offshore oil and gas business in particular. To address this concern, a quantitative model capable of predicting occupational accident frequency in the offshore petroleum industry is being developed. The model offers a means to optimise safety spending, thereby maximising benefits to personnel and the organisation. As input to the development of the model, industry safety experts were polled with respect to the relative importance of a series of influencing factors. These included elements directly affecting accidents, organisational issues, and external factors. A questionnaire was the chosen method to consolidate industry opinion. The results of the questionnaire are presented here, together with a discussion, interpretation, and conclusions.     

Accident investigation in the Norwegian petroleum industry - Common features and future challenges

In their regulations, the Petroleum Safety Authority Norway (PSA) states that the companies responsible for oil and gas exploration and production activities in Norway shall record and investigate accidents and serious incidents. This paper introduces a general model for the accident investigation process in the Norwegian petroleum industry, and presents some results derived from a comparative study of causal factors identified in offshore accident investigation reports from 2002 to 2006. The paper also offers a discussion of the improvement potential in the use of investigation methods. Finally, a discussion of future challenges in accident investigations is provided.