Reliability analysis of subsea blowout preventers with condition-based maintenance using stochastic Petri nets

Blowout Preventer (BOP) has maintained its function as a safety barrier and the last line of defence against oil and gas spills since its development in the early 1900s. However, as drilling and exploration activities move further offshore, challenges pertaining to reliable operation of the subsea BOP systems continue to be a source of concern for stakeholders in the industry. In spite of recent advancements in reliability analysis of safety instrumented systems (SISs), the research on reliability assessment of BOP is still lacking in some regards. There are gaps in the literature with respect to the incorporation of preventive maintenance (PM) strategies as well as dynamic operating conditions into BOP reliability analysis. To address these gaps, this paper develops an advanced analysis method using stochastic Petri nets (SPN) to estimate the reliability of subsea BOP systems subject to condition-based maintenance (CBM) with different failure modes. The BOP system is divided into five subsystems which are connected in series with each other and categorised into degrading and binary units. The performance of the BOP system in terms of availability, reliability and mean-time-between failures (MTBF) is obtained and analysed. A sensitivity analysis is also performed to evaluate the effect of fault coverage factor and redundancy design on system performance. The results show that both the fault coverage factor and redundancy have significant impact on the BOP's reliability, availability and MTBF.     

Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures

A subsea blowout preventer (BOP) stack is used to seal, control and monitor oil and gas wells. It can be regarded as a series–parallel system consisting of several subsystems. This paper develops the dynamic Bayesian network (DBN) of a parallel system with n components, taking account of common cause failures and imperfect coverage. Multiple error shock model is used to model common cause failures. Based on the proposed generic model, DBNs of the two commonly used stack types, namely the conventional BOP and modern BOP are developed. In order to evaluate the effects of the failure rates and coverage factor on the reliability and availability of the stacks, sensitivity analysis is performed.     

Dynamic Bayesian networks for reliability evaluation of subsea wellhead connector during service life based on Monte Carlo method

The subsea wellhead connector is a critical connection component between subsea Christmas tree and subsea wellhead for preventing the leakage of oil and gas in the subsea production system. Excited by cyclical loadings due to environmental forces and the other support forces, the subsea wellhead connector is prone to the failure, which could lead to the loss of subsea tree or wellhead integrity and even catastrophic accidents. With the Monte Carlo simulation method, this paper presents a reliability analysis approach based on dynamic Bayesian Networks, aiming to assess the failure probability of the subsea wellhead connector during service life. Take the driving ring component of the subsea wellhead connector as an example to demonstrate the reasonability of the proposed model. The generation data is processed by the transform between the numerical value and the state variable. Based on the stress-strength interference theory, the structure reliability of the driving ring with 96.26% is achieved by the proposed model with the consideration the aging of the material strength and the most influential factors are figured out. Meanwhile, the corresponding control measures are proposed effectively reduce the failure risk of the subsea wellhead connector during service life.     

Reliability analysis of subsea blowout preventer control systems subjected to multiple error shocks

Two configurations of subsea blowout preventer (BOP) distributed control systems, which are triple modular redundancy (TMR) control system and double dual modular redundancy (DDMR) control system, are presented. With respect to common-cause failures, the performances of the two systems are evaluated by using Markov method with multiple error shock model. Due to the complexity, each system is split into three independent modules, and the corresponding Markov models are proposed subsequently. The probability of failure on demand, availability and reliability of the systems are evaluated by merging the independent Markov modules by Kronecker product approach. The results indicate that a same safety integrity level of SIL3 can be attained for the two configurations, which satisfies the requirement of subsea BOP control system, even though both of them have some advantages and shortcomings. In addition, for TMR control system, the effects of multiplicity distribution of multiple error shock and mean time to repair on reliability performances are studied.     

基于马尔可夫方法的水下防喷器可靠性研究

水下防喷器是保障海上钻井安全的关键设备,对其可靠性进行定量评价对井控作业有重要的指导意义.为了弥补现有水下防喷器可靠性评价方法的不足,将水下防喷器的工作状态分为四种,包括无故障可用、关井、井控关键失效及关井时失效.利用Markov方法建立了水下防喷器的Markov模型.通过水下防喷器系统的状态转换图找出了各工作状态的转换关系.通过分析墨西哥湾83口深水井水下防喷器的失效数据,定义了影响水下防喷器可靠性的井控关键失效,并对深水钻井水下防喷器防喷功能的可靠性进行了定量计算.将计算结果与不考虑关井期间的井控关键失效相比较发现,防喷器的防喷失效概率增加了65％.因此传统的定量评价方法可能会得出相对乐观的结论,应在实际生产中给予重视.     

基于马尔可夫的地面控制井下安全阀可靠性评估

为保障地面控制井下安全阀系统的安全运行，防止系统发生故障，建立了井下安全阀可修复系统的马尔可夫模型；针对系统设备构成复杂及共因故障等问题，基于β因子模型描述共因失效，同时将模型划分为3个独立模块，通过克罗内克积方法合并，评估系统可靠性；参照OREDA可靠性数据，定量求解井下安全阀系统可用度、可靠度以及稳态指标，研究模型中状态转移概率对系统稳态可用度的影响。研究结果表明:井下安全阀系统的可用度随时间增长而迅速到达稳态值；系统检修周期应小于2.5 a；根据可靠性分析结果，运营方应考虑系统经济与可靠性间的博弈关系，合理优化系统冗余结构与维修周期管理，防止井下安全阀系统失效。     

基于Isograph的地铁车载ATP系统动态故障树分析

针对传统的可靠性分析方法在具有动态特性的地铁车载ATP系统可靠性分析中的不足,提出动态故障树分析法。在引用可用度、平均失效时间和平均修复时间等可靠性指标的基础上,结合Isograph软件改进动态故障树分析法,建立基于Isograph的动态故障树分析模型,并通过对车载ATP系统的结构与功能分析以及历史故障数据的统计分析确定车载ATP系统的关键部件,使用 Isograph软件预计关键部件的可靠性参数,对地铁车载ATP系统的可靠性进行评估。结果表明:采用该方法求解的系统可靠性指标均满足系统需求,并且精确度较高,运用此方法能够对具有顺序相关、资源共享、可修复以及冷、热备份等特性的系统进行可靠性建模,达到快速以及准确分析动态特性系统可靠性的目的。     

Integrated framework to optimize RAM and cost decisions in a process plant

The research implications of reliability, availability and maintainability (RAM) aspects of engineering systems in recent years have increased substantially due to rising operation and maintenance costs. To strike a balance between the two the paper presents a framework which makes use of both qualitative and quantitative techniques to optimize RAM and cost decisions in a process plant. In the quantitative analysis, the imprecise and vague information regarding the system failure behavior is quantified by using the principles of fuzzy mathematics in terms of fuzzy and crisp values. Further, to manage the system reliability for best economic performance a resource optimization model based on multi-stage decision making (MSDM) has been proposed. The model makes use of crisp output values of unit's reliability along with relevant system information (number of components, manpower, cost ranges). In the qualitative analysis the in-depth analysis of the system is carried out using Root Cause analysis (RCA) and Failure Mode & Effects Analysis (FMEA). The ambiguities associated with the traditional FMEA are handled using Fuzzy Decision Making System (FDMS) and Grey Relation Analysis (GRA). The suggested framework has been illustrated with the help of a case.     

Research and application of risk and condition based maintenance task optimization technology in an oil transfer station

Oil transfer stations of PetroChina mostly scatter in Gobi, mountain areas or other sparsely populated areas, inconvenient transportation and absent professional engineers often delay the best time to repair the machines. Time-or interval-based maintenance (TBM) accounts for almost 100%, while, On-condition maintenance and other proactive maintenance are seldom adopted. TBM not only can't prevent happens of equipment fault but also cause the waste of the maintenance resource. In order to allocate maintenance resources reasonably, ascertain the minimum preventive maintenance requirement, ensure the reliability, availability and safety, this paper carries out a research on Risk and Condition Based Maintenance (RCBM) task optimization technology. Utilizing the internet of things (IOT), real-time database, signal-processing, Gray Neural Network, probability statistical analysis and service oriented architecture (SOA) technology, a Risk and Condition Based Indicator Decision-making System (RCBIDS) is built. RCBIDS integrates RCM, condition monitoring system (CMS), key performance management module, file management module, fault and defect management module, maintenance management module together, which aims to realize remote condition monitoring, maintenance technical support services (TSS), quantitative maintenance decision-making, and to ensure the Reliability, Availability, Maintainability and Safety (RAMS). The Predictive Maintenance Indicator model, reliability prediction model and Key Performance Indicator (KPI) model, which are embedded in the RCBIDS, are constructed separately. An engineering case shows that the risk and condition based maintenance task optimization technology can be used to optimize maintenance content and maintenance period, to minimize maintenance deficiencies and maintenance surplus, and to prolong the lifespan of equipment.     

Reliability evaluation of fire alarm systems using dynamic Bayesian networks and fuzzy fault tree analysis

In almost all industries, fire alarm systems play a vital role in the reducing the risks associated with fires and damages. Therefore, it is necessary to evaluate their reliability and performance in emergency situations. The present study aimed to use fault tree analysis (FTA) to determine the root causes involved in the failure of fire alarm systems, to use Fuzzy set theory and expert elicitation to determine relative probabilities, and finally, to evaluate the reliability of a fire alarm system using dynamic Bayesian networks (BNs) during a thirty-six months period. A total of 29 basic events were detected from the FT. The reliability of the fire alarm system was estimated at 0.954 according to the FT and 0.957 according to conventional BNs. The reliability of the fire alarm system after 36 months was estimated at 0.375 according to dynamic BNs. All the events involved in the failure of fire alarm systems were drawn in the fault tree diagram. The results indicate that remodeling of these systems and simultaneous construction activities are the most important factors in the failure of the fire alarm system. System reliability can also be increased to 0.965 by providing preventive and control measures to reduce the probability of critical events.     

油气管道腐蚀可靠性的贝叶斯评价法

对油气管道腐蚀危害因素进行分析,建立其失效故障树。根据故障树分析原理,找出导致管道腐蚀穿孔破坏的23个因素。通过对故障树的定性分析,采用下行法求出油气管道腐蚀失效故障树的96个全部最小割集,并确定失效的主要影响因素。结合最小割集不相交化法和贝叶斯可靠性评定法对管道腐蚀失效进行定量分析,通过某油气管道事故统计数据,利用贝叶斯可靠性评定方法求出油气管道腐蚀可靠性的一阶矩和二阶矩。对一阶矩和二阶矩进行拟合,求出油气管道腐蚀可靠性的第一近似下限和第二近似下限。结果表明:得出的油气腐蚀管道贝叶斯可靠性评价结果可以指导管道系统的维护和维修,降低管道运行的风险。     

Component joint importance measures for maintenances in submarine blowout preventer system

In order to improve the performance and maintain the interconnection components of the subsea pipeline ram blowout preventer system, the idea of importance analysis can be used to solve this problem. This paper uses an extended joint integrated importance measure to effectively analyze the characteristics of component failures of the blowout preventer system. The interaction between two components is considered to improve system performance while the failed component is being repaired. In order to facilitate subsequent maintenance and repair work, the effects of changes in different parameters on the importance value are considered. Finally, the analysis of a numerical example of the submarine pipeline ram blowout preventer system is carried out to verify the proposed method.     

成套装置动态风险管理专家系统

传统的设备管理模式造成设备非计划停机次数较多、故障频繁、可靠性和可用性不高等问题。为了解决上述问题,开发了成套装置动态风险管理专家系统,该系统包括动态风险监控、数据存储、失效模式及损伤机理判别、动态风险评估、风险辅助分析5个流程。该系统通过GIS平台进行展示,使用户可以直观、方便地查找、定位管线和容器位置,实现了高风险设备的风险展示、管道剩余寿命不足报警功能和管道冲蚀图例展示。将该专家系统进行了工程应用,得到容器和管道的潜在损伤机及其风险等级,针对不同风险等级的设备,生成了不同的检维修策略,为工程应用带来了很大的方便。     

系统安全性与可靠性分析的故障树方法

探讨了大型复杂系统安全性与可靠性分析的故障树技术。阐述了国外具有代表性的几种建树方法。针对实际应用情况,提出了将建树过程划分为5个阶段,应遵循的8条基本准則。导出了典型系统可靠度与故障率等可靠性参数计算的通用公式。论述了故障树分析方法的特点及发展动向。     

Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis

Vast amounts of oil & gas (O&G) are consumed around the world everyday that are mainly transported and distributed through pipelines. Only in Canada, the total length of O&G pipelines is approximately 100,000 km, which is the third largest in the world. Integrity of these pipelines is of primary interest to O&G companies, consultants, governmental agencies, consumers and other stakeholder due to adverse consequences and heavy financial losses in case of system failure. Fault tree analysis (FTA) and event tree analysis (ETA) are two graphical techniques used to perform risk analysis, where FTA represents causes (likelihood) and ETA represents consequences of a failure event. ‘Bow-tie’ is an approach that integrates a fault tree (on the left side) and an event tree (on the right side) to represent causes, threat (hazards) and consequences in a common platform. Traditional ‘bow-tie’ approach is not able to characterize model uncertainty that arises due to assumption of independence among different risk events. In this paper, in order to deal with vagueness of the data, the fuzzy logic is employed to derive fuzzy probabilities (likelihood) of basic events in fault tree and to estimate fuzzy probabilities (likelihood) of output event consequences. The study also explores how interdependencies among various factors might influence analysis results and introduces fuzzy utility value (FUV) to perform risk assessment for natural gas pipelines using triple bottom line (TBL) sustainability criteria, namely, social, environmental and economical consequences. The present study aims to help owners of transmission and distribution pipeline companies in risk management and decision-making to consider multi-dimensional consequences that may arise from pipeline failures. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process. A simple example is used to demonstrate the proposed approach.     

A reliability evaluation method based on the weakest failure modes for side-by-side offloading mooring system of FPSO

Offshore structures are complex systems, and numerous failure modes must be taken into consideration when reliability analysis in different loads and environment conditions are conducted on them. It is difficult to obtain structural system reliability with respect to complicated systems with numerous failure modes and dependency consideration among them. This paper applies the combination of the weakest failure modes theory with structural reliability theory to conduct reliability analysis on of side-by-side offloading mooring system of FPSO. Firstly, the numerical simulation of the system in different conditions is addressed to acquire the statistical data of time-history stress of components including hawsers, fenders and yoke, based on which, the reliability indexes of all the failure modes and correlation coefficient matrix are derived. Then the weakest failure modes, i.e. the representative failure modes that have significant impact on the system, are located through Probability Network Evaluation Technique. The probability of structural system is estimated through the weakest failure modes by considering the system as series. The analysis results indicate that two environment conditions (0°&0°&0° and 0°&30°&45° in combinations of wave, wind and current) are relative dangerous, which is in good correspondence with the practical expertise. The method is verified to be an effective and convenient evaluation approach for structural reliability analysis in terms of complex systems. It is beneficial for the identification of structure indicators from the weakest failure mode group and conduct optimum of structural system configurations in the design stage.     

Recursive Operability Analysis as a decision support tool for Risk-Based Maintenance

This work was developed with the support of MEMC, one of the most important producers of ultra-pure silicon wafers for electronic applications throughout the world. The availability of ultra-pure water is of prime importance in the silicon production process. In order to maximize the availability of UP water, MEMC has developed a preventive maintenance program and a detailed record of each maintenance intervention is recorded. This has allowed a complete failure rate data bank to be developed. In order to optimize the maintenance intervention, a Recursive Operability Analysis (ROA) has been used as a decision support tool. The results of the ROA, coupled with the failure rates, have made it possible to calculate the expected number of events (ENE) of various top events (TEs). The magnitude of each TE has been estimated on the basis of the monetary losses provoked by each event. The risk then has been calculated and the events ranked on this basis. Maintenance policies have been optimized with the aim of reducing the risk of the top ranked events.     

Development and application of equipment maintenance and safety integrity management system

Equipment management in process industry in China essentially belongs to the traditional breakdown maintenance pattern, and the basic inspection/maintenance decision-making is weak. Equipment inspection/maintenance tasks are mainly based on the empirical or qualitative method, and it lacks identification and classification of critical equipment, so that maintenance resources can’t be reasonably allocated. Reliability, availability and safety of equipment are difficult to control and guarantee due to the existing maintenance deficiencies, maintenance surplus, potential danger and possible accidents. In order to ensure stable production and reduce operation cost, equipment maintenance and safety integrity management system (MSI) is established in this paper, which integrates ERP, MES, RBI, RCM, SIL and PMIS together. MSI can provide dynamic risk rank data, predictive maintenance data and RAM decision-making data, through which the personnel at all levels can grasp the risk state of equipment timely and accurately and optimize maintenance schedules to support the decision-making. The result of an engineering case shows that the system can improve reliability, availability, and safety, lower failure frequency, decrease failure consequences and make full use of maintenance resources, thus achieving the reasonable and positive result.     

基于模糊评判和RCM的CTCS-3级车地通信子系统维修决策

CTCS-3级车地通信子系统是列控系统的重要组成部分之一,且列控系统需求的列车位置、行车许可、临时限速等安全信息都由其提供,因此,对其进行可靠性分析具有十分重要的意义。采用贝叶斯网络对系统进行可靠性分析,并对维修方式进行了讨论。首先,根据车地通信子系统的功能与结构构建其贝叶斯网络模型。然后,综合考虑维修性、共因失效等因素,对车地通信子系统进行可靠性分析。最后,在可靠性分析的基础上运用模糊综合评判法对其进行维修决策。结果表明:利用贝叶斯网络的双向推理,不仅可以计算出车地通信子系统的可靠度,还可以有效识别系统的薄弱环节;若不考虑车地通信系统冗余结构中的共因失效,则得到的可靠性指标会偏于乐观。地面GSM-R单元失效是引起车地通信子系统失效的关键事件,因此针对此薄弱环节进行状态维修检查能够有效降低事故的发生概率。     

Mitigation of operational failures via an economic framework of reliability,availability, and maintainability (RAM) during conceptual design

Abnormal process situation may lead to tremendous negative impact on sustainability, wellbeing of workers and adjacent communities, company's profit, and stability of supply chains. Failure of equipment and process subsystems are among the primary causes of abnormal situations. The conventional approach in handling failure-based abnormal situations has usually focused on operational strategies. Such an approach overlooks the critical role of process design in mitigating failure, while simultaneously considering the effects of such failure on process economic performance. The aim of this work is to introduce a systematic methodology that accounts for failure early enough during the conceptual design stages. Once a base-case design is developed, the methodology starts by identifying the sources of failure that are caused by reliability issues including equipment, operational procedures, and human errors for a given process system or subsystem. This allows for the identification of critical process subsystem(s) that are more failure-prone or cause greater downtime than other subsystems. Bayesian updating and Monte Carlo techniques are utilized to determine the appropriate distributions for the failure and repair scenario(s), respectively, in question. Markov analysis is used to determine the system availability. Next, the process revenue is described as a function of inherent availability. The effects of failures are incorporated into profitability calculations to establish an economic framework for trading off failure and profitability. In the proposed framework, the economic potential of alternative design scenarios is evaluated and an optimization formulation with the objective of maximizing incremental return on investment (IROI) is utilized to make a design decision. A case study on an ethylene plant is solved to demonstrate the applicability and value of the proposed approach.