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.     

Fault diagnosis of pipeline and pump unit systems using status coupling analysis

Earlier studies on fault diagnosis of the pipeline and pump unit systems (PPU) relied mainly on independent equipment analyses, which usually lead to false alarms because of the loss of information fusion. The aim of this study is to utilize the status coupling relationship to improve fault detection sensitivity and reduce false alarm rate. A real-time status identification of related equipment step is added between capturing abnormal signals and listing out diagnosis results. For example, when the pipeline pressure fluctuation is found abnormal, a status analysis of pump units is performed immediately, if the pump units are proven to be operational normally, then the pipeline leak alarm is acknowledged valid. The logical reasoning algorithm is used to capture abnormal conditions of pipeline pressures. The pump unit faults are captured by combining information from multiple sources. Field applications show that the proposed method significantly improves the PPU fault detection capability on fault detection sensitive and accuracy.     

Reliability and availability modeling of Subsea Xmas tree system using Dynamic Bayesian network with different maintenance methods

Subsea Xmas tree is a vital equipment for offshore oil and gas development. Aiming at the fault mode of subsea Christmas tree system under production conditions, the fault tree of subsea tree system was established, which was transformed into Dynamic Bayesian network, and the reliability and availability of subsea tree system with different repair states are quantitatively analyzed. In this paper, the DBNs are partially verified by the method based on three axes. The results show that the reliability of subsea vertical tree system is slightly higher than that of subsea horizontal tree system. After repair and maintenance, the performance of subsea tree system has been significantly improved, and the improvement of the system performance by preventive maintenance is more obvious. Compared with the perfect repair, the performance of the system with imperfect repair is not significantly reduced. Compared with perfect repair & preventive maintenance, the performance of the system with imperfect repair & preventive maintenance is slightly reduced. In addition, the influence of failure rates and degradation probability on reliability and availability is analyzed. By comparing the influence of failure rates on the system performance of non-maintenance and maintenance, it is found that the change of failure rates has the greatest influence on the reliability and the least influence on the availability of perfect repair & preventive maintenance. By comparing the performance of each component in the subsea tree system, it is found that the failure rates has the most obvious influence on the chock module, and gate valve and tree cap have the most significant influence on the reliability of the system. In order to improve the reliability of subsea tree system, it is necessary to improve the reliability of chock module, gate valve and tree cap.     

故障诊断专家帮助系统开发及其在电厂脱硝设备中的应用

总结SCR和SNCR两种脱硝系统中常见的21种故障,将人工智能专家系统与脱硝系统的专业知识、故障解决绑定。在Windows开发环境下,使用Visual Basis 6.0,以Access建立专家知识库和数据库,开发出脱硝设备故障诊断专家帮助系统软件。专家帮助系统的主要功能有:查询脱硝系统知识并及时更新数据库,现象分析,诊断与解决故障。     

外部安防系统在海洋石油固定平台中的应用

为了弥补传统的海洋石油固定平台内部安防系统的不足,达到海洋石油固定平台全天候自动监测、自动报警、无人值守、主动防御、预防为主的目的,以便提早发现灾害或事故的苗头,提供及时报警,并采取适当的预防措施。根据主动防御、准确测报、防范未然和规避事故的原则方针,按区域警戒与要地防范相结合的方法,介绍了外部安防系统在海洋石油固定平台中的应用,包括六个子系统、工作流程、各个子系统在海洋石油固定平台中的应用以及特点和价值,从监控、应急、监管等多角度出发,实现了一体化的安全监控。     

基于系统动力学的海洋平台安全脆弱性分析

海洋平台承受着风、浪、流等复杂环境载荷，安全系统在保障平台安全稳定方面的作用极为突出。为深入探讨影响海洋平台安全脆弱性的机理，针对海洋平台作业环境的极端性和复杂性，建立了海洋平台的安全脆弱性系统动力学分析模型，采用模糊层次分析法确定影响安全系统中各层因素的权重系数，并设定参数，考虑子系统不同的安全投入方案，进行了海洋平台脆弱性系统分析。研究结果表明:海洋平台设备系统对于暴露度和敏感度的影响程度最高，管理系统对海洋平台安全系统的适应度起主导作用；当安全投入总值一定，对各系统的投入比例不同时，为有效降低海洋平台脆弱性，应合理分配有限投入资金。     

信息融合技术在锅炉故障诊断中的应用

为满足锅炉故障诊断的要求，用信息融合技术，提出了具有数据挖掘功能的锅炉故障诊断系统的一般模型。在对锅炉故障诊断中的信息进行分类的基础上，详细阐述了模型中各功能模块的作用以及它们之间的层次关系，并提出了模型中关键技术的实现方法。实例分析表明，信息融合技术用于锅炉故障诊断对提高诊断的可靠性和准确性有重要作用。     

基于STM32F103ZET6的自定义通信协议电梯监控系统设计

针对电梯运行状态监控问题,提出一种基于自定义通信协议的电梯远程监控系统的设计方案。以STM32F103ZET6为控制核心的电梯监测终端,通过对传感器信号进行检测分析获得电梯运行状态及故障的信息,监测服务平台与电梯监测终端之间采用自定义的通信协议进行数据通信,监测服务平台将所获取的电梯状态及故障信息发送到云计算中心服务器。用户通过客户端登录电梯监控系统,可以实时查看电梯的运行状态、故障信息及历史信息。通过长期实际应用,该监控系统运行稳定可靠,能够实现对电梯远程的实时、可靠监控,为电梯的维修、维护提供及时准确的信息。     

A review on different pipeline fault detection methods

Pipeline faults like leakage and blockage always create problem for engineers. Detection of exact fault quantity and its location is necessary for smooth functioning of a plant or industry and safety of the environment. In this paper brief discussion is made on various pipeline fault detection methods viz. Vibration analysis, Pulse echo methodology, Acoustic techniques, Negative pressure wave based leak detection system, Support Vector Machine (SVM) based pipeline leakage detection, Interferometric fibre sensor based leak detection, Filter Diagonalization Method (FDM), etc. In this paper merit and demerits of all methods are discussed. It is found that these methods have been applied for specific fluids like oil, gas and water, for different layout patterns like straight and zigzag, for various lengths of pipeline like short and long and also depending on various operating conditions. Therefore, a comparison among all methods has been done based on their applicability. Among all fault detection methods, Acoustic reflectometry is found most suitable because of its proficiency to identify blockages and leakage in pipe as small as 1% of its diameter. Moreover this method is economical and applicable for straight, zigzag and long, short length pipes for low, medium and high density fluid.     

Damage to offshore oil and gas facilities following hurricanes Katrina and Rita: An overview

Hurricanes Katrina and Rita hit the centre of the American petrochemical industry, shutting down eight refineries, hundreds of oil-drilling and production platforms, and many other industrial facilities. Furthermore, it triggered unprecedented numbers of hazardous-materials releases from industrial facilities and storage terminals onshore, as well as from oil and gas production facilities offshore in the Gulf of Mexico (GoM). In this paper, we analyse the damage caused by the two hurricanes on the offshore oil and gas industry. Hurricanes Katrina and Rita caused the largest number of destroyed and damaged platforms and pipelines, and the highest number of mobile offshore drilling units set adrift in the history of GoM operations. Following the hurricanes, changes have been proposed to operating and emergency procedures, maintenance requirements, and design practices including mooring practices for mobile offshore drilling units.     

The oil and gas industry,the competence assessment of Offshore Installation Managers (OIMs) and Control Room Operators (CROs) in emergency response,and the lack of effective assessment of underpinning technical knowledge and understanding

Within the UKCS offshore oil and gas industry there exist key personnel who perform critical roles in an emergency as part of the installation's emergency response team (ERT), two of these personnel are the Offshore Installation Manager (OIM) and the offshore Control Room Operator (CRO). The OIM is responsible for the safety and well-being of all persons onboard and within 500 m of the installation. Should an emergency situation arise, the OIM performs one of the most critical roles in emergency response - management of the response to the emergency to mitigate and make safe the installation to secure a place of safety for persons onboard or organise their safe evacuation. Such emergency response shall also include limiting the loss of hydrocarbons to the environment. The CRO monitors and interprets the output from an installation's Distributed Control System (DCS) and Fire and Gas Panel (FGP), responding to alarms and deviations from steady state. In many instances, an offshore CRO's actions in response to these deviations can determine whether or not the situation deteriorates into a disaster. Should an emergency occur, the CRO will act in accordance with an installation's emergency response procedures and OIM instructions.The persistence of incidents, a number leading to disasters, in the industry highlights the importance of both OIM and CRO competence in controlling emergencies. Both positions require an effective system to assess the competence of job holders. Research at the University of Aberdeen identified potential barriers that can prevent the effective competence assessment of an OIM in emergency response. An extension to this research analyses the two offshore industry standards for competence in emergency response: OPITO 7025 – OIM Controlling Emergencies and OPITO 9004 – Control Room Operator Emergency Response, and associated documentation, applying a critical hermeneutic approach, focusing on the assessment of underpinning knowledge and understanding within the roles. The key findings are that there currently exists no effective means to assess the underpinning technical knowledge and understanding of either OIMs or CROs in emergency response within the offshore industry. Knowledge and, in particular, understanding are poorly defined within the relevant OPITO standards. There exists no validation of evidence collated to support an individual's underpinning knowledge and understanding and no truly independent audit of collated evidence or the process of assessment.The research is important to Duty Holders (see also OSD Installation Operators under UK Offshore Safety Directive Regulations) and employers within the offshore oil and gas industry, organisations associated with the offshore industry, safety engineers and UK Government departments with regard to the effectiveness of competence assessment of appointed OIMs and CROs. It is also significant to risk engineers when considering the probable competence of key emergency response personnel in a given environment at a given point in time. The research is not just applicable to the UKCS offshore industry but also to the global offshore oil and gas industry.     

基于Petri网的750kV变电站故障恢复

对750 kV变电站可能发生的常见故障进行详细分类,并据此建立故障恢复推理模型,以便发生故障时能及时快速恢复供电。通过分析750 k V变电站拓扑结构,采用故障分类和基于Petri网建模的方法,将所有可能发生的常见故障分为母线、线路及变压器3类。变电站发生故障时,先根据变电站故障诊断结果,再从故障点隔离到恢复非故障失电区域并建立Petri网模型。故障恢复主要对开关进行操作,因此首先建立开关打开和关断的基本Petri网恢复模块,接着从变压器运行方式及裕量情况给出变压器故障恢复的Petri网模型,然后讨论母线和线路故障时不同情况下的故障恢复Petri网模型,最后根据模型推理过程中点火的变迁,将隔离故障点和恢复供电的断路器记录下来,按照Petri网运行的顺序即可给出最终的恢复方案。通过对750 k V变电站各类复杂故障的建模和推理及由此给出的恢复方案,能够及时给出恢复的操作顺序,方便运行人员快速检修。最后通过HPSim软件对代表性故障进行仿真运行,验证该方法正确、可行且及时。     

基于模糊Petri网的有载分接开关故障诊断方法研究*

为解决分接开关故障诊断以主观经验、缺乏系统化流程以及诊断结果与分接开关实际发生故障存在偏差等问题,依据当前分接开关主要故障分类,提出基于模糊Petri网的有载分接开关故障诊断模型,并结合分接开关典型故障案例,验证模型有效性。研究结果表明:基于模糊Petri网的分接开关故障诊断模型能够有效处理故障概率中不确定性因素,具有容错性好、运行效率高等优势,研究结果可为提高分接开关故障诊断的准确性、保障电力系统安全稳定运行提供参考。     

海上生产设施远程监控和管理系统的建设探讨

针对海洋石油行业面临的海上作业设施如何安全有效监控及管理的问题,从实际的安全生产管理需求出发,结合通信网络链路及传输特点,采用先进的远程监控技术和海洋石油应急管理理念,设计开发了海上生产设施远程监控和管理系统平台,并论述了总体框架、软硬件结构及发展趋势。系统已经作为保障安全生产的重要手段应用于海上作业平台及船舶运输,提高了安全生产管理水平,并取得了很好的效果。     

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.     

Comprehensive alarm information processing technology with application in petrochemical plant

During the abnormal plant conditions, too much information is produced due to momentary plant excursions above alarm limits. This flood of information impedes correct interpretation and correction of plant conditions by the operator. Existing techniques for the design of alarm systems mostly have weak ability to handle complex hazard scenarios and increase the probability of larger safety issues. In this paper, a comprehensive alarm information processing (AIP) technology is introduced, called multi-round alarm management system (MRAMS), including several processing strategies: AIP based on single sensor, AIP based on sensor group, root cause diagnosis based on Bayesian network, sensor fault judgment method and false alarm inhibition method. In case studies, both simulation experiment and pilot application on a real petrochemical plant are presented. Results indicate the MRAMS is helpful in improving the accuracy of correctly diagnosing the root causes and hence avoiding false and redundant alarms. By adopting this new technology, the safe and reliable operation of the plant can be achieved, and the economic loss brought by improper alarms can be reduced.     

燃煤电厂袋式除尘专家系统开发研究

开发了一种袋式除尘系统故障专家诊断方法,用于诊断整个袋式除尘系统的故障现象,通过现场数据采集、操作人员与专家系统的人机对话对故障现象进行分析、推理,并做出相应的解决方案指导操作人员排查故障.燃煤电厂袋式除尘专家系统以知识库、推理机为核心,实现整个袋式除尘系统的设备故障诊断功能和维修故障指导功能,辅以解释机构,人机界面来指导用户如何准确无误地操作运行本系统和袋式除尘控制系统.系统被划分为4个模块:专家系统简介模块,在线故障诊断模块,离线故障诊断模块和专家指导模块.     

An integrated off-on line approach for increasing stability and effectiveness of automated controlled systems based on pump dependability—case study: Offshore industry

Automated controlled systems are vulnerable to faults. Faults can be amplified by the closed loop control systems and they can develop into malfunction of the loop. A control loop failure will easily cause production stop or malfunction at a petrochemical plant. A way to achieve a stable and effective automated system is to enhance equipment dependability. This paper presents a standard methodology for the analysis and improvement of pump performance to enhance total operational effectiveness and stability in offshore industry based on dependability. Furthermore, it is shown how a reliability–safety analysis can be conducted through equipment dependability indicators to facilitate the mitigation of hazard frequency in a plant. The main idea is to employ principle component analysis (PCA) and importance analysis (IA) to provide insight on the pumps performance. The pumps of offshore industries are considered according to OREDA classification. The approach identifies the critical pump and their fault through which the major hazards could initiate in the process. At first PCA is used for assessing the performance of the pumps and ranking them. IA is then performed for the worst pump which could have most impact on the overall system effectiveness to classify their components based on the component criticality measures (CCM). The analysis of the classified components can ferret out the leading causes and common-cause events to pave a way toward improving pump performance through design optimization and online fault detection which ultimately enhance overall operational effectiveness.     

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.     

Integrating probabilistic signed digraph and reliability analysis for alarm signal optimization in chemical plant

The alarm system given in industrial plants are massive and complex. Under such condition, critical alarms are overwhelmed by false and unnecessary alarms and thus result in severe safety issues. To address the problem, this paper proposes a probabilistic signed digraph (PSDG) based alarm signal selection method that requires achieving maximal system reliability. In this method, a PSDG model is firstly constructed to visualize the causal relations between process variables. Then the criteria of observability and identifiability are imposed to determine the candidate alarm variables that can qualitatively distinguish all assumed faults. Instead of selecting the minimum number of combinations of candidate variables, the alarm variables are optimized by a reliability formulation that takes into account the missed alarm and false alarm probabilities of the system; this formulation is solved by the receiver operating characteristic (ROC) graph. Finally, the developed methodology is illustrated using a Tennessee Eastman process.