Corrosion failure probability analysis of buried gas pipelines based on subset simulation

Corrosion is the main reason for the failure of buried gas pipelines. For effective corrosion failure probability analysis, the structural reliability theory was adopted in this study to establish two calculation models for pipeline corrosion failure: the pressure failure model and von Mises stress failure model. Then, two calculation models for the corrosion failure probability were established based on a corrosion depth growth model obtained from actual survey data of soil corrosion characteristics. In an example, Monte Carlo simulation (MCS) and subset simulation (SS) were used to analyze the corrosion failure probability of pipelines, and the results were compared. SS can compensate for the shortcomings of MCS as it has higher computational efficiency and accuracy. Therefore, SS was adopted to simulate variations in the corrosion failure probability of buried pipelines with the service time for the two failure probability calculation models, which were applied to a natural gas pipeline located in a chemical industry park in Zhuhai, China. A sensitivity analysis was carried out on the relevant parameters that affect the failure probability. The results showed that multiple loads caused by the covering soil, residual stress, temperature differential, and bending stress have a non-negligible effect on the pipeline reliability. The corrosion coefficients gradually become the most important factors that affect the failure probability with increased service time. The proposed methodology considers the actual operating conditions of pipelines to provide a reliable theoretical basis for integrity management.     

Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field

Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.     

A hybrid machine learning model for predicting crater width formed by explosions of natural gas pipelines

Despite the existence of industry models for estimating the crater width formed by the explosion of natural gas pipelines, their applicability is still limited since the complex formation mechanisms. In this work, a novel hybrid model was developed to predict crater width formed by explosions of natural gas pipelines, using artificial neural networks (ANN) as the fundamental predictor. Based on the historical accident records, the proposed hybrid model was trained by the pipeline parameter, the operating condition, the installation parameter, and the crater width. A novel nature-inspired optimization algorithm, i.e., the Lévy-Weighted Quantum particle swarm optimization (LWQPSO) algorithm, was proposed to optimize the ANN model's parameters. Three machine learning models were developed for comparative reasons to predict the crater width. The use of precision and error analysis indicators assesses prediction performance. The results show that the proposed hybrid model (LWQPSO-ANN) has high prediction accuracy and stability, which outperforms QPSO-ANN-based benchmark hybrid models and the model without an optimizer (Support Vector Machine, SVM). The parameter sensitivities of the proposed algorithm, including the maximum number of iterations, population size and contraction-expansion coefficient, were determined. The proposed hybrid model is expected to support the quantitative risk assessment (QRA), Right-of-Way (ROW) definition and the inherently safer design of the underground parallel pipelines.     

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.     

基于一维卷积神经网络的燃气管道泄漏声发射信号识别*

为保障燃气管道系统安全运行,及时诊断管道故障,基于VGG-16模型提出基于一维卷积神经网络的燃气管道故障诊断模型,提取原始声发射信号特征参数,有效诊断燃气管道故障。结果表明:基于一维卷积神经网络的燃气管道故障诊断模型,能够有效解决燃气管道故障诊断过程中数据预处理复杂、特征提取困难以及识别准确率低等问题,可为燃气管道故障诊断提供技术支撑。     

An integrated methodology to manage risk factors of aging urban oil and gas pipelines

Aging urban oil and gas pipelines have a high failure probability due to their structural degradation and external interference. The operational safety of the aging urban oil and gas pipeline is challenged by different hazards. This paper proposes a novel methodology by integrating an index-based risk evaluation system and fuzzy TOPSIS model for risk management of aging urban oil and gas pipelines, and it is carried out by evaluating the priority of hazards affecting pipeline safety. Firstly, the hazard factors of aging urban oil and gas pipelines are identified to establish an index-based risk evaluation system. Subsequently, the fuzzy TOPSIS model is employed to evaluate the importance of these hazard factors and to decide which factors should be managed with priority. This work measures the importance of a hazard factor from three aspects, i.e. occurrence (O), severity (S) and detectability (D), and the weights of these three parameters are determined by a combination weight method. Eventually, the proposed methodology is tested by an industrial case to illustrate its effectiveness, and some safety strategies to reduce the operational risk of the pipeline are presented. The proposed methodology is a useful tool to implement more efficient risk management of aging urban oil and gas pipelines.     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

基于BP网络的建筑安装施工现场安全综合评价的研究

针对目前我国建筑安装施工现场安全评价技术的不成熟和欠科学性的现状 ,笔者分析和综合了目前安全评价技术 ,结合建筑业特点 ,提出了基于BP神经网络的建筑安装施工现场安全评价方法 ,并对该评价模型的原理、方法及算法进行了研究。首先 ,结合建筑安装施工现场安全生产的特点建立评价指标体系 ,随后 ,运用层次分析法确定指标及准则层的权重 ,并运用模糊综合评价法生成评价样本集 ,最后 ,利用样本集训练BP网络 ,待误差满足要求后 ,即可运用训练成功的BP神经网络进行安全评价。     

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.     

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.     

基于突变理论的采空塌陷区输气管道危险性评价研究

为了减少采空塌陷区输气管道危险性评价中对主观判断的依赖,提出以突变理论为基础的危险性评价模型。首先,从采矿因素、岩体物理力学参数、环境与地质因素和埋地管道因素等4个方面分析采空塌陷区输气管道危险性影响因素;其次,建立采空塌陷区输气管道危险性指标体系;然后,对指标体系中的底层指标进行无量纲化和归一化处理;最后,计算得到输气管道危险性突变数值,进而实现对采空塌陷区输气管道危险性等级的综合评判。将以上评价模型进行实例应用,应用结果表明,基于突变理论的采空塌陷区输气管道综合评价模型可以用于采空塌陷区输气管道的危险性评价。     

基于AE的燃气管网事故分析模块设计与实现

随着城市燃气管网的飞速发展,在役燃气管道的安全问题日益突出,针对燃气事故致因及后果,基于ArcGIS网络模型,运用GIS网络拓扑分析、空间数据库等技术,设计利用管网网络模型实现管网事故分析方法,并结合C#+ArcEngine编程技术实现燃气管网事故分析模块,该模块实现爆管分析、燃气泄漏扩散范围分析、最优路径分析、连通性...     

燃气管网定量风险分析方法综述

以城市燃气管网的风险为研究对象,分析并提出一种可用于城市燃气管网定量风险分析的新思路,包含了不同事故后果及其物理模型的分析即事故可能性分析、后果分析和风险评价,分为失效事故假定、泄漏率计算、物理效应计算、致死率计算、风险值计算、风险评价等环节;整理、研究城市燃气管网定量风险分析所涉及的多种物理模型,并通过比较不同模型的特点,分析各个模型的不足之处;最后针对国内外研究现状及燃气管网风险的特点,指出研究发展方向:研究风险在燃气管网内的传播,提出燃气管网相继失效的风险分析方法。所提出的分析思路、计算方法可与工程应用相结合。     

常州市燃气管网破坏的人工神经网络预测模型

作为一种高效清洁的能源,燃气已经成为城市能源中的重要一员,燃气管网破坏亦成为城市所面临的重大安全隐患。城市埋地燃气管网的破坏风险,往往受到多种影响因素的共同作用。通过分析常州市埋地燃气管网破坏的影响因素,确定了地面沉降、地裂缝、城市内涝、土壤腐蚀等4个风险评价因子。运用MATLAB中的人工神经网络工具,通过人工神经网络计算,优化了模型网络结构,建立了常州市埋地燃气管网破坏风险预测的人工神经网络模型。分析计算结果,并为常州市埋地燃气管网的安全防护提供了建议。     

天然气管道完整性管理探析

通过对国内外天然气管道事故的统计和分析,指出了对天然气管道进行完整性管理的重要性;在此基础上,简要介绍了管道完整性管理方法,着重探讨了天然气管道完整性管理的流程和环节,包括管道潜在危害辨识、管道数据信息搜集与分析、管道风险评价、管道完整性评价、完整性评价响应和减缓措施,并进一步说明了天然气管道完整性管理的实施方法;最后,给出了在我国开展天然气管道完整性管理的必要性和建议.     

The method for leakage detection of urban natural gas pipeline based on the improved ITA and ALO

To solve the problems of the difficulty in early leakage monitoring and larger positioning error for urban hazardous chemicals pipelines, the optimized method based on the improved Inverse Transient Analysis (ITA) and Ant Lion Optimizer (ALO) was proposed. Firstly, based on the obtained experiment's results of leakage of natural gas in the non-metallic pipeline, the segment classification method was incorporated into the pressure gradient calculation. The modified method can adapt to the multi-node characteristics of urban pipe networks and help to obtain the preliminary positioning calculation results after optimization. Then the calculation results were embedded in the ITA calculation model. The input parameters of the gas pipeline such as boundary conditions, leakage rate and friction coefficient were used to establish the characteristic linear equations. Then the objective function of the least-squares criterion was defined, and the improved ITA model suitable for leakage detection of urban natural gas pipeline networks was constructed. Finally, the ALO was used to optimize the calculation process of the improved ITA model, and iteratively optimize the optimal friction coefficient and its corresponding minimum objective function (OF) value. As a result, a more precise location of the leakage source was calculated. The validation of the modified method is conducted by comparing the calculated values with the experiment's results. The results show that the method can accurately predict the location where the pipeline leakage occurs. The minimum error is 3.17%. Compared with the traditional ITA, this method not only accelerates the convergence speed of the objective function, but also improves the accuracy of location calculation.     

兰成渝腐蚀管道失效压力的GA-BP 神经网络组合预测方法

为准确掌握管道失效压力,保证管道安全运行,根据神经网络的非线性和良好的函数逼近特性,提出了基于遗传算法（GA）优化的BP神 经网络组合模型的腐蚀长输管道失效压力预测模型。组合模型将最佳组合阀值与权值隐含在网络的连接中,兼具遗传算法、人工神经网络预测 的优点,并克服了原始数据少对预测精度的影响,同时避免了神经网络容易陷入局部寻优的缺陷,也增强了网络的适应性,改善网络的收敛性 ,在客观地反应腐蚀油气管道失效压力变化趋势方面具有一定的优势。通过实例分析,结果表明:BP神经网络的预测值和Modified B31G计算结 果与真实值误差均较大,而GA-BP的预测值与实际结果的相对误差最大为6.12%,有很好的一致性,为管道的预防性维修提供了理论依据。     

基于TVR的腐蚀油气管道失效概率及安全寿命研究

为避免因腐蚀导致油气管道失效,针对因管道特性和腐蚀尺寸的不确定性使得管道剩余强度成为概率模型的特点,建立了腐蚀管道强度损失随机模型;借助可靠性理论,通过分析管道腐蚀进程的时变性特点,将管道系统由损伤积累和抗力衰减导致的剩余强度随机化;提出基于穿越率的腐蚀油气管道失效评定及安全寿命预测方法。研究结果表明:腐蚀速率和运行压力对管道失效概率及安全寿命影响显著,管道尺寸影响适中,而相关系数和拉伸强度影响较小;若腐蚀速率Va=0.2 mm/a,VL=10 mm/a或局部腐蚀缺陷半径达到管道壁厚的0.5倍时,建议作为重点风险段监测并检修。所建方法是对腐蚀油气管道运营监控和风险评估的有益补充。     

连续起伏管道投产过程中的气相运移研究*

为应对山区液体管道在投产过程中可能出现的气阻、超压问题,从气相运移角度出发,建立液顶气模型,研究在1个U型单元内积气形成、压缩和破碎的全过程,在此基础上,提出连接各个U型单元的气相的传递函数,探讨背压累积因素下,连续起伏管道投产过程中各个U型管段的积气情况和压力的变化,进行动态的建模和计算。以国内某原油管道的现场投产数据与模型结果进行比对。结果表明:可以更加准确地预测山区液体管道投产过程中的气相传递和压力变化过程,能为未来连续起伏大落差液体管道投产的安全稳定运行提供理论指导和技术支持。     

Application of game theory in risk management of urban natural gas pipelines

This paper presents a game theory methodology for risk management of urban natural gas pipelines, which is a collaborative participation mechanism of the stakeholders, including government, pipeline companies, and the public. Firstly, the involvement proportion of stakeholders in risk management under rational conditions is estimated by the static game theory. Subsequently, the system dynamics (SD) simulation is used to establish an evolution game model of stakeholders in risk management under the irrational conditions, in which the stability of the evolution game process is analyzed. The stakeholders’ involvement proportions from the static game model are utilized as the inputs for the evolution game model to simulate the dynamic evolution behavior of risk management strategies with different involvement proportions of stakeholders. Eventually, the dynamic evaluation game can extract an optimal strategy for risk management of urban natural gas pipelines. A case study is used to illustrate the methodology. In essence, this methodology can be extended for implementing risk management of urban infrastructure.