Analysis of pitting corrosion on steel under insulation in marine environments

Corrosion under insulation (CUI) is an important issue in marine environments. Pitting corrosion is a significant contributor to this issue. The ability to understand and model pitting behavior is integral to designing and maintaining assets in marine environments to decreased costs and increase safety and productivity. This paper reviews and analyses six categories of pitting knowledge to assess the current depth and breadth of understanding and to identify knowledge gaps in each category. The categories investigated are: identification of pitting, experimental methods, mechanism of pitting, modeling of pitting corrosion rates, remaining life assessment modeling, and risk-based inspections. This analysis finds that the depth of knowledge on pitting corrosion rate modeling and pitting mechanism is limited and requires further detailed study. The outcome of such study will strengthen pitting corrosion rate modeling, the accuracy of fitness-for-service assessments and risk-based inspection strategies.     

基于遗传-神经网络算法的含均匀腐蚀缺陷油气管线爆破压力预测研究*

为提高含均匀腐蚀缺陷油气管线爆破压力的预测精度，保障长输油气管线的安全运行，将遗传算法和BP神经网络相结合，建立含均匀腐蚀缺陷油气管线爆破压力预测的遗传-BP神经网络（GA-BPNNs）模型。采用已有文献实验数据，分析对比该模型与AGA NG-18，ASME B31G，修正B31G，PCORRC，DNV RP-F101和SHELL 92等方法用于X46，X52，X60，X65，X80等材质油气管线含均匀腐蚀缺陷时爆破压力的计算误差。结果表明:GA-BPNNs模型用于含均匀腐蚀缺陷油气管线爆破压力预测时，误差在-7.78%～6.06%之间，预测精度明显高于目前国内外通用规范的计算结果；该模型操作简单，适用范围广，工程实用性好，为含缺陷压力管道爆破压力的预测提供更好的思路和方案。     

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.     

Modelling of pitting corrosion in marine and offshore steel structures – A technical review

Corrosion is a major cause of structural deterioration in marine and offshore structures. It affects the life of process equipment and pipelines, and can result in structural failure, leakage, product loss, environmental pollution and the loss of life. Pitting corrosion is regarded as one of the most hazardous forms of corrosion for marine and offshore structures. The total loss of the structure might be very small, but local rate of attack can be very large and can lead to early catastrophic failure. Pitting corrosion is a localized accelerated dissolution of metal that occurs as a result of a breakdown in the protective passive film on the metal surface. It has been studied for many years; however, the structural failure due to pit characteristics is still not fully understood. Accurate pit depth measurements, precise strength assessment techniques, risk analysis due to pitting, and the mathematical relationship of the environmental factors that causes pitting failure are also factors, which need further understanding. Hence this paper focuses on these issues. It reviews and analyses the current understanding of the pitting corrosion mechanism and investigates all possible factors that can cause pitting corrosion. Furthermore, different techniques employed by scientists and researchers to identify and model the pitting corrosion are also reviewed and analysed. Future work should involve an in-depth scientific study of the corrosion mechanism and an engineering predictive model is recommended in order to assess failure, and thereby attempt to increase the remaining life of offshore assets.     

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。     

基于改进PSO-BPNN的输油管道内腐蚀速率研究

为解决输油管道易腐蚀,且腐蚀程度难以测量的问题,提出使用改进的粒子群算法（PSO）优化误差反向传播神经网络（BPNN）对输油管道内腐蚀速率进行预测。改进的PSO算法提升了自身搜索到全局最优的能力,可为BPNN提供最优初始权值和阈值,从而有效避免BPNN易陷入局部最优的问题发生。以某条输油管线为例,分别运用标准的BPNN模型、PSO-BPNN以及改进的PSO-BPNN对该管线内腐蚀速率进行预测。结果表明:基于改进的PSO-BPNN的预测结果平均相对误差为5.57%,预测精度较BPNN和PSO-BPNN有明显提升。使用改进的PSO-BPNN预测输油管道的腐蚀速率可为管道的检测维修提供可靠的理论和技术支撑。     

基于有限元的含腐蚀缺陷P110油管失效分析与安全评价

含有腐蚀缺陷油管在复杂的工作荷载下容易发生失效。建立了在内压与轴向力共同作用的复杂工作载荷下,含椭球型蚀坑缺陷与轴向沟槽型缺陷两种腐蚀缺陷的油管有限元模型。基于该模型研究了不同载荷工况下缺陷宽度、深度与长度对油管安全性及失效模式的影响。研究结果表明:腐蚀位置深度较浅时,椭球型蚀坑缺陷更危险,油管主要发生由轴力引发的断裂失效;腐蚀位置深度较深时,轴向沟槽型缺陷更危险,主要发生由内压引发的破裂失效。研究成果可为含腐蚀缺陷油管的安全评估提供依据。     

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.     

埋地油气管道沿线土壤腐蚀性可拓评价研究

油气管道长期埋设在土壤环境中会受到各种侵蚀介质的腐蚀并引发泄漏、火灾、爆炸等事故。针对土壤腐蚀因素复杂多变的特点,建立基于物元、可拓集合和关联函数的埋地油气管道沿线土壤腐蚀性评价模型;选取土壤电阻率、自然电位、氧化还原电位、pH值、含水率、Cl-含量、SO42-含量7个评价指标,将土壤对油气管道腐蚀的严重程度划分为低、较低、中、强和较强5个等级;采用客观赋权的变异系数法（CV法）来确定土壤腐蚀性评价指标的权重,避免了以往评价方法的主观性,进一步提高模型的精度和效率。以某埋地天然气管道5个管段土壤为例,可拓评价结果表明4#土壤的腐蚀性较强,其他土壤腐蚀性中等,这与外腐蚀检测结果吻合,证明所想出的模型简便、高效、精确,具有很好的科学性和工程应用性。     

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.     

基于光电子能谱的高含水输油管道内壁腐蚀减薄试验分析

鄂尔多斯盆地高含水输油管道内壁腐蚀减薄现象普遍,腐蚀穿孔频次逐年上升,管道安全防护管理成为难题。为了识别输油管道内壁腐蚀减薄原因,作者在分析含水率、地层水水型、溶解氧对管道内腐蚀影响的基础上选择特征试验管段,基于X射线光电子能谱技术开展管道内壁厚度变化、金相组织、腐蚀产物特性、边缘腐蚀状态研究。结果表明:高含水输油管道内壁腐蚀减薄类型为Cl+O2+H2O环境下的垢下腐蚀,以溶解氧（O2）腐蚀为主,氯离子（Cl－）腐蚀为辅;内壁腐蚀减薄原因为原油含水率过高、水中氯离子含量过高、水中含有溶解氧和介质流速偏低。     

水改气海底管道输送高压天然气可行性研究

为了满足油气输送需求,现将一输水管线改输高压天然气,需对该管线改输的可行性进行研究论证,通过ANSYS建模分析得到腐蚀缺陷处在475MPa高压下所受到的最大等效应力小于管道的设计抗力2736MPa,满足安全标准。利用OLGA模拟出管道输送高压天然气时的压力和温度曲线满足实际工况要求,OLGA模拟出的预测腐蚀速率最大处为004mm/y,对管道的剩余寿命影响较小。应用DNV腐蚀因素失效概率模型计算得到该管道的失效概率小于15×10-3,安全等级介于“一般”和“很高”之间。经研究论证得该管道改输高压天然     

基于差异进化支持向量机的坑外土体沉降预测

就用支持向量机(SVM)预测基坑外土体沉降而言,通过差异进化(DE)算法构造适合的决策函数十分重要。在确定坑外土体沉降函数的基本形式下,进行参数反演。后将得到的解析式作为SVM的决策函数,再进行核函数转换,从而使SVM的曲线拟合更加快速,预测更加准确。对大连地铁湾家车站基坑坑外土体的沉降数据的分析及预测的结果表明,使用SVM-DE算法在计算数据量、计算消耗时间和预测精度方面优于2种方法单独使用。     

CO2 corrosion control in steel pipelines. Influence of turbulent flow on the performance of corrosion inhibitors

Corrosion associated with aqueous environments containing carbon dioxide (CO₂) and/or hydrogen sulphide (H₂S), is a well-known phenomenon in oil and gas industries. This type of corrosion is of particular importance in transportation through steel pipelines. This transportation process could involve the movement of a complex mixture of gas and liquids. This moving mixture is in close contact with the inner surface of the steel pipelines and corrosion can occur. It has been demonstrated that this corrosion is influenced by flow.In oil and gas industries, film-forming corrosion inhibitors are the main tool used to control inner corrosion in pipelines. The movement of the environment generates mechanical shear stresses on the surface of the steel that can interfere with the formation of the film. This phenomenon is frequently not taken into account in corrosion control strategies and could cause problems. Despite the importance of this, there are few scientific studies available, which can provide control criteria.This work presents some ideas developed in order to understand the influence of flow on the corrosion process, making emphasis in the corrosion process associated with carbon dioxide (CO₂).     

A mathematical model of crevice corrosion for buried pipeline with disbonded coatings under cathodic protection

Crevice corrosion occurs in a holiday and disbonded region between coating and pipeline steel. Cathodic protection (CP) is generally recognized as the most effective method for corrosion prevention of pipeline, but its effectiveness may be reduced at defects in a disbonded coating. It is difficult to measure and probe corrosion parameters accurately based on experimental work. Therefore, a mathematical model is necessary to identify the phenomena and mechanisms that contribute to the crevice corrosion process. In this work, a mathematical model was developed to determine the evolution of chemical and electrochemical transient processes of crevice corrosion in NaCl dilute solution, and the effect of cathodic protection and crevice width on corrosion of 20# steel pipeline with disbonded coatings. Results have demonstrated that the extent of crevice corrosion depends on the crevice geometry and could be influenced by the increase of crevice depth and decrease of its width. The oxygen concentrations drop significantly inside a crevice whether CP is applied or not and whether crevice width decreased or not. The pH values and conductivity of crevice solution increase with the time. The research provides a theoretical foundation for cathodic protection of pipelines and establishes an effective corrosion model which can identify the phenomena and mechanisms of the crevice corrosion process. This work could be used to help mitigate the corrosion failure of pipelines to prevent catastrophic accidents in oil, gas and chemical process industries.     

基于SPH-FEM耦合法的含缺陷输气管道爆炸冲击响应研究

针对大口径埋地输气管道发生物理爆炸对并行含体积缺陷邻管的冲击行为，利用LS-DYNA和LS-PREPOST有限元软件建立基于光滑粒子流体动力学-有限单元法的管-土-炸药耦合模型，分析不同缺陷深度、不同缺陷表面积、不同缺陷位置和不同爆心距下邻管的动力响应；基于爆腔预估公式和峰值振速经验公式，验证了所建耦合模型的可靠性，并通过设计算例开展多工况分析。研究结果表明:迎爆面上的缺陷处为动力响应的热点区域，最大响应特征值（应力、位移与振速）位于缺陷中心处，随缺陷深度的增加或管间距的减小特征值增速由平缓到急剧；相比缺陷位置和表面尺寸对管道的扰动程度，缺陷深度和爆心距对管道的动力响应影响较大；在本研究的条件下，建议埋地并行输气管道的安全间距不应小于5.16 m，且腐蚀深度不大于管道壁厚的0.633 6倍。研究结果可为埋地输气管道极端灾害下的风险评估提供技术支撑，为并行管道可能的抗爆隔爆设计提供模拟数据支持。     

基于IAHP和灾变链式方法的城市油气管道风险诱因与预警研究

城市油气管道穿越城区街道、建筑和居民区等特殊地段,保障其安全运行具有重要意义。为实现城市油气管道风险早期预警,基于城市与野外长输油气管道风险对比分析,识别城市油气管道风险预警指标;建立城市油气管道风险预警指标体系,采用区间层次分析法对预警指标重要度进行定量排序,确定关键预警监测点;并依据灾变链式理论,构建城市油气管道重大事故灾变链式模型,研究管道风险演化过程,发现灾变前兆进行断链减灾。研究结果表明:“腐蚀”及“第三方破坏”占据城市油气管道失效致因比重最大,风险因子“油气管道与市政管道距离”以及“城市工程施工作业”应作为城市油气管道重点监测点。同时,围绕城市油气管道风险预警需致力于孕源断链。     

腐蚀管道评价标准ASME B31G-2009和SY/T6151-2009对比

腐蚀是油气管道最常见的失效形式之一,对腐蚀管道进行剩余强度评价是保障管道安全可靠运行的基础工作。ASME B31G-2009和SY/T6151-2009是国内外的两种主流评价标准的最新版本,为了在选取适当的评价标准时有判断依据,比较分析了新版标准评价结果的保守程度,研究了评价流程、计算模型和计算参数取值等方面,并结合爆破试验数据和有限元仿真结果进行了验证。结果表明,两种标准仍具有一定的保守性,且当管道强度等级、腐蚀情况及数据资料完整度等不同时,两种标准有各自的适用范围和优缺点。     

基于改进K-means和CNN的储罐罐底点蚀诊断模型*

为解决储罐罐底点蚀问题,提出基于改进K-means和CNN的储罐罐底点蚀诊断模型,在传统聚类基础上引入肘部法则,保证k值选取3的准确性,将原始声发射信号特征参数和聚类后的类别信息输入模型进行训练,系统预测准确率高达99%。研究结果表明:该模型能够及时发现点蚀现象,指导管理者确定储罐开罐检查时间顺序,避免点蚀穿孔造成的人力、物力损失,降低储罐运行风险,保障储罐运行安全,研究结果可为罐底点蚀诊断提供技术支撑。     

IAFSA-GRNN在油田集输管道CO2腐蚀速率预测中的应用

为提高油田集输管道CO₂腐蚀速率预测的准确性,针对原始广义回归神经网络(GRNN)预测精度低的问题,提出改进的群智能算法优化原始GRNN的预测模型;分别使用GRNN模型、人工鱼群算法(AFSA)优化的GRNN(AFSA-GRNN)模型和自适应改进的AFSA-GRNN(IAFSA-GRNN)模型预测X65管线钢的CO₂腐蚀速率。结果表明:采用AFSA和IAFSA优化光滑因子S后,能大大提高GRNN模型的预测精度,预测结果的平均相对误差由36.09%分别减小至7.20%和6.90%;与AFSA相比,IAFSA优化的GRNN不仅具有更高的预测精度,还具有更快的收敛速度。AFSA-GRNN在第164次迭代计算时收敛,而IAFSA-GRNN在第109次迭代计算时收敛,说明AFSA经自适应优化能提高优化过程的收敛速度和GRNN的预测精度。