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.     

Improvement of integrity management for pressure vessels based on risk assessment - A natural gas separator case study

The process of oil and gas processing plant is complex, the types of pressure vessels are rich, and the functions are critical. However, the working medium is mostly untreated medium, and the hazard factors are complex, which poses a threat to the safe production of oil and gas processing plant. Based on PDCA cycle, this paper establishes a six-step links of integrity management for sustainable improvement of pressure vessels. The typical failure modes of pressure vessels are determined, and the fishbone diagram of risk factors under each failure mode is compiled. Risk quantification and classification of pressure vessels based on failure modes (RBFM) is innovatively proposed. Avoiding incalculable failure frequency index, the process quantification of failure possibility is formed according to the development of hazard factors. A failure consequence calculation model based on the leakage affected area was established. Combined with the failure probability level and risk level, the hierarchical inspection strategy for pressure vessels under different failure modes is established. Finally, the method is applied to the natural gas separator of H processing plant. The research results show that RBFM proposed in this paper can meet the requirements for rapid and accurate risk assessment of pressure vessels in oil and gas processing plant. This paper establishes a safe production barrier for the pressure vessel and improves the intrinsic safety of the equipment.     

油气火灾爆炸事故评价及防范措施

油气火灾爆炸事故对人类自身和企业所造成的危害日益受到人们的重视 ,因此 ,有必要加强对含有油气的环境进行危险评价和科学管理。笔者应用可能性分布的事故树分析法 ,对引发油气火灾爆炸事故的可能因素进行了定量分析 ,然后对油气火灾爆炸事故进行了评价 ,通过评价对存在的问题提出了相应的防范措施。     

Simulation-based fault propagation analysis—Application on hydrogen production plant

Recently production of hydrogen from water through the Cu–Cl thermochemical cycle is developed as a new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact and reduced greenhouse gas emissions. Considering these advantages, the usage of this technology in new industries such as nuclear and oil is increasingly developed. Due to hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability and risk assessment. However, very little research is done from safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using evolutionary techniques like genetic programming (GP) and neural networks (NN), to uncover process variables’ interactions. The effectiveness, feasibility and robustness of the proposed method are demonstrated on simulated data obtained from the simulation of hydrogen production process in Aspen HYSYS^®. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.     

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.     

2 000 m超深水水下分离器泄漏油气扩散特性研究

深水水下分离器服役过程中由于腐蚀、地质灾害和环境高压作用,存在失效泄漏风险。针对2 000 m超深水水下分离器可能存在的失效泄漏问题,基于计算流体动力学CFD方法,建立分离器失效泄漏后果数值仿真分析模型,对分离器泄漏场景进行模拟与分析,研究2 000 m水深条件下分离器泄漏油气扩散规律,并考虑不同泄漏位置对油气扩散行为的影响。结果表明:水下分离器泄漏包括压力扩散和自由扩散两个阶段,压力扩散阶段历时极短,自由扩散阶段耗时较长;泄漏口位置对泄漏结果有较为明显的影响,分离器上部泄漏,油气全部溢出,分离器中下部泄漏,大部分油气保留于分离器内部,最终形成分明的油气水界面;分离器内部压力随时间迅速上升,t=0.25 s左右接近于20 MPa,后期在20 MPa左右呈极微小波动,泄漏速率随分离器内部压力增大迅速减小,达到最低点之后,呈微小波动状变化。     

A new fault detection method for non-Gaussian process based on robust independent component analysis

Conventional fault detection method based on fast independent component analysis (FastICA) is sensitive to outliers in the modeling data and thus may perform poorly under the adverse effects of outliers. To solve such problem, a new fault detection method for non-Gaussian process based on robust independent component analysis (RobustICA) is proposed in this paper. A RobustICA algorithm which can effectively reduce the effects of outliers is firstly developed to estimate the mixing matrix and extract non-Gaussian feature called independent components (ICs) by robust whitening and robust determination of the maximum non-Gaussian directions. Furthermore, a monitoring statistic for each extracted IC is constructed to detect process faults. Simulations on a simple example of the mixing matrix estimation and a fault detection example in the continuous stirred tank reactor system demonstrate that the RobustICA achieves much higher estimation accuracy for the mixing matrix and the ICs than the commonly used FastICA algorithm, and the RobustICA-based fault detection method outperforms the conventional FastICA-based fault detection method in terms of the fault detection time and fault detection rate.     

Investigating new risk reduction and mitigation in the oil and gas industry

The complexity of the processes and the nature of volatile petroleum products urged the oil and gas industry to utilize various risk assessment techniques to identify potential failure modes that can interrupt operation processes. Consequently, government agencies and nonprofit professional societies guide the industry with regulatory guidelines, standards, and best recommended practices to oversee the operations management, assure safe working environment, and contain failures within tolerable limits. Yet, accidents due to electro-mechanical failures still occur and result in various consequences. Accordingly, critics have raised concerns about the petroleum industry's safety and risk mitigation credentials and question its ability to prevent future major accidents. Therefore, new risk assessment tools need to be introduced to provide decision makers and novice engineers with a diverse perception of potential risks. The aim of this paper is verify the application of Risk in Early Design (RED), a product risk assessment tool, in identifying potential failures in the oil and gas industry. Approximately thirty major accident underwent the RED analysis to verify the software's application to identify and rank potential failure modes.     

加油站油流静电在线监测技术及应用

为提高加油站安全作业水平,分析了卸油、加油过程中油流静电起电因素和常用检测方法。根据高斯定理从理论上探讨了通过测量输油管道内中心电位得到油流静电电荷密度的检测原理,并基于杆球传感器以及压电传感器技术设计开发了油品静电在线监测仪,实现了对加油枪前后、加油机内部管线及过滤器油品带电情况的在线监测。测试结果表明:基于此静电监测技术,建立加油站油流电荷监测与联锁控制措施,可有效避免油品静电燃爆事故的发生,保障加油站安全生产。     

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.     

A new leak location method based on leakage acoustic waves for oil and gas pipelines

In order to study a new leak detection and location method for oil and natural gas pipelines based on acoustic waves, the propagation model is established and modified. Firstly, the propagation law in theory is obtained by analyzing the damping impact factors which cause the attenuation. Then, the dominant-energy frequency bands of leakage acoustic waves are obtained through experiments by wavelet transform analysis. Thirdly, the actual propagation model is modified by the correction factor based on the dominant-energy frequency bands. Then a new leak detection and location method is proposed based on the propagation law which is validated by the experiments for oil pipelines. Finally, the conclusions and the method are applied to the gas pipelines in experiments. The results indicate: the modified propagation model can be established by the experimental method; the new leak location method is effective and can be applied to both oil and gas pipelines and it has advantages over the traditional location method based on the velocity and the time difference. Conclusions can be drawn that the new leak detection and location method can effectively and accurately detect and locate the leakages in oil and natural gas pipelines.     

Cost-based fire risk assessment in natural gas industry by means of fuzzy FTA and ETA

Fire is the most prevalent accident in natural gas facilities. In order to assess the risk of fire in a gas processing plant, a fault tree analysis (FTA) and event tree analysis (ETA) has been developed in this paper. By utilizing FTA and ETA, the paths leading to an outcome event would be visually demonstrated. The framework was applied to a case study of processing plant in South Pars gas complex. All major underlying causes of fire accident in a gas processing facility determined through a process hazard analysis (PHA). Fuzzy logic has been employed to derive likelihood of basic events in FTA from uncertain opinion of experts. The outcome events in event tree has been simulated by computer model to evaluate their severity. In the proposed methodology the calculated risk has the unit of cost per year which allows the decision makers to discern the benefit of their investment in safety measures and risk mitigation.     

油气管道伴行光缆失效统计及对策措施研究

油气管道伴行光缆在油气运输中起着十分重要的作用。由于外部环境的突发性、复杂性、不可控性,导致伴行光缆管理困难、混乱,断缆事件经常发生,给油气管道运输带来较大损失。本文以某公司为例,以2012~2018年光缆断裂事故数据为依托,运用鱼刺图方法梳理出光缆失效的4大原因类别,并应用故障树分析法对光缆失效进行分析,从最小割(径)集、结构重要度方面对光缆失效因素分析,并针对不同的危害因素提出合理的风险管控措施,构建光缆失效保护层,为今后油气管道伴行光缆风险管控提供指导。     

Model-based information fusion investigation on fault isolation of subsea systems considering the interaction among subsystems and sensors

The offshore oil industry has expanded to deep water and Arctic. The harsh operating conditions (e.g., ice and strong wind) and increasing complicated system raise the occurrence likelihood of system faults. This requires timely fault isolation and management in the subsea system. However, the offshore oil industry mainly relies on humans to isolate faults based on alarms. With harsh operating conditions and increasing complicated system, this industry urgently needs research on more efficient fault isolation and cause diagnosis methods. Unfortunately, limited research is conducted on fault isolation method in the offshore oil industry. Furthermore, in industry 4.0 era, large amounts of information are obtained. This provides precondition for the application of information fusion technique which aims to improve diagnosis results. However, to the authors’ knowledge, information fusion has not been much studied in the fault isolation of the offshore oil industry. Moreover, the interaction of different subsystems contains valuable information. How the interaction of different subsystems can influence the fault diagnosis has not been explored. This paper proposes a Bayesian network (BN) based method for timely fault isolation and cause diagnosis for the offshore oil industry. The work fuses different information, and it also includes the dependency among different subsystems in the fault diagnosis. As an important alarm source, false alarms are also taken into account in the model. A case study on the subject of the subsea wellhead and chemical injection systems is conducted to demonstrate the functions and merits of the proposed method.     

海上采油油轮增设油气分离装置的探讨

胜利油田海上边际油井受环境条件的制约,安装采油的油气分离装置也较简单;由此产出的原油中含有20%天然气进入油轮,给整个采油的油轮带来危险并给操作人员带来危害。在采油油轮上增设油气处理装置后,既改善了油轮操作人员的工作环境,消除了油轮事故隐患,又节约了在原平台上增设油气分离设施的费用,保护了采油油轮操作者的安全与健康,保障了安全生产。笔者依据胜利油田海上采油油轮的生产作业状况,论述在采油油轮上增设油气分离装置的必要性,并对所增设的设备及施工条件作了初步探讨。同时还论证了海上采油油轮增设油气分离装置,是改善操作人员的工作环境和消除事故隐患的必要措施。     

齿轮的振动故障研究

齿轮是机械设备中重要的传动部件 ,广泛应用于汽车、船舶、飞机、农机等机械设备中 ,其质量好坏直接影响整机设备的性能 ,它的失效是诱发机器故障的重要因素。笔者从理论上研讨了齿轮故障的产生机理 ,列举了影响齿轮产生振动的因素 ,并仿真出了齿轮存在不同故障的振动信号图形 ,为诊断齿轮的故障提供了重要的依据。因此 ,可以参考仿真图形与实际图形的对比 ,得出齿轮的故障所在 ,并为此提出了对齿轮振动信号进行处理的各种合适方法 ,同时这些方法也适用于其他大型设备的故障诊断。     

City hazardous gas monitoring network

In our today's societies, many dangerous chemicals are produced and transported. Due to the vast use of chemicals, more chemical accidents are taking place with huge losses. In this study a city hazardous gas monitoring network was designed to detect the dispersion of toxic and combustible gases in the primary stages. The network could cover hazardous chemical facilities, important hazardous chemical routes, warehouses and special locations which may be the targets of terrorist attacks. The network is consisted of several local networks and a central control panel complex. Each local network has a local control panel in the center and many detectors and sounders around it at distances less than 3000 m that communicate with the local control panels wirelessly. In each location there are two types of gas detectors, toxic and combustible, and a sounder which are equipped with a wireless, radio frequency modem allowing the units to communicate readings and other information on a real-time basis with a remotely located local control panel. High sensitive Photo Ionization Detectors, PIDs, are used to provide fast and low-level on-site screening for chemicals contamination. Combustible gas detectors are the second choice to sense the combustible gas and verify the readings of PIDs in this regard. The central panel consists of several connected control panels work uniquely helping a computer set and the appropriate software and communicate with local control panels via telephone lines. All of the network components are shown on the monitor of central panel with special symbols by geographical information system program. The system is fully addressable so that the high level detection of a detector produces a blinking color double-circle around its symbol in GIS plan. In case of high level gas detection, a team of experts who are fully equipped with different portable detectors depart to the site to test the field to identify the chemicals. All readings of detectors are saved in a data bank and then analyzed to find any chemicals spills and leakages. The network was simulated by a special program so that the components of local networks and the central panel are shown in separate windows. By clicking on one detector on environmental window the formerly designed responses will be activated in central panel window.     

随钻压力波在用于早期气侵检测时的扰动传播特性研究*

为了研究随钻测量装置（Measurement While Drilling,MWD）压力波信号在用于早期气侵检测时的扰动传播特性,基于油气井多相流流动理论,建立随钻压力波在环空气液两相流中的扰动传播模型,对多参数影响下的压力波传播与衰减特性进行模拟,并对压力波检测技术的现场应用效果进行分析。结果表明:含气率、角频率、系统压力、虚拟质量力、拖曳力和壁面剪切力的变化都会对压力波在环空气液两相流中的传播与衰减特性造成不同程度的影响;相比于常规的全烃量检测技术,压力波检测技术可以更早地检测到气侵的发生,可进一步提高油气井建井的安全性。     

Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting

The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, corrosions and cracks due to the contact of oil particles with the pipeline surface. Hence, the structural integrity of these pipelines is of great interest due to the probable environmental, infrastructural and financial losses in case of structural failure. Based on the existing technology, it is difficult to analyze the risks at the initial stage, since traditional methods are only appropriate for static accident analyses. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. To sort out the above problem in the oil pipelines, fault identification and prediction methods based on K-means clustering and Time-series forecasting incorporated with linear regression algorithm using multiple pressure data are proposed in this paper. The real-time validation of the proposed technique is validated using a scaled-down experimental hardware lab setup resembling characteristics exhibited by onshore unburied pipeline in India. In the proposed work, crack and blockages are identified by taking pressure rise and pressure drop inferred from two cluster assignment. The obtained numerical results from K-means clustering unveils that maximum datasets accumulated range of multiple pressures are within 16.147–10.638 kg/cm², 14.922–12.1674 kg/cm², 2.7645–1.2063 kg/cm² correspondingly. Hence by this final cluster center data, inspection engineers able to estimate the normal and abnormal performance of oil transportation in a simple-robust manner. The developed forecast model successfully predicts future fault occurrences rate followed by dissimilarity rate from clustering results holds the validity of 91.9% when applied to the historical pressure datasets. The models are expected to help pipeline operators without complex computation processing to assess and predict the condition of existing oil pipelines and hence prioritize the planning of their inspection and rehabilitation.     

Common cause failures in safety instrumented systems on oil and gas installations: Implementing defense measures through function testing

This paper presents a common cause failure (CCF) defense approach for safety instrumented systems (SIS) in the oil and gas industry. The SIS normally operates in the low demand mode, which means that regular testing and inspection are required to reveal SIS failures. The CCF defense approach comprises checklists and analytical tools which may be integrated with current approaches for function testing, inspection and follow-up. The paper focuses on how defense measures may be implemented to increase awareness of CCFs, to improve the ability to detect CCFs, and to avoid introducing new CCFs. The CCF defense approach may also be applicable for other industry sectors.