海底管线腐蚀检测与腐蚀预测的研究
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摘要
本论文研究内容来源于国家高技术研究发展计划(863计划)——渤海大油田勘探开发关键技术重大专项“海底管道外探测装置及其检测技术”(2004AA602210)。
海洋是一个腐蚀性极强的环境,各种钢铁设施在海洋环境中极易发生腐蚀破坏。在海洋环境下传输油气的金属管线腐蚀远比陆地上要严重,物理因素、化学因素和生物因素均可导致管线的腐蚀,而且一旦发生腐蚀破坏,维修极为困难,后果不堪设想。由于海底管线的腐蚀是一种长期缓慢的过程,随着管线服役时间的增加,管线的腐蚀状态如何?牺牲阳极能否处于正常的工作状态?能否达到设计的保护电流密度?其结果都将严重影响管线在设计年限的正常运转。因此研究海水环境中常用金属管线的腐蚀特征、实时检测管线腐蚀状态、预测管线腐蚀速度,对管线的阴极保护系统的设计、运行维护都有重要的意义。尽管目前我国还没有水深超过200米的海底石油天然气管线,随着油气开采范围的拓展,深海油气传输将成为现实问题,所以深海管线腐蚀检测方法的研究,无论技术上还是在理论上都具有具有重要意义。
本论文首次提出一种非接触式检测阴极保护下海底管线腐蚀状态的方法和非线性预测海底管线腐蚀速度的模型。主要研究内容分为海底管线阴极保护电场仿真计算、不接触测量管线电位和海底管线腐蚀速度预测三部分。论文首先应用边界元理论仿真计算管线周围环境电场和管线表面的分布电位,建立相应的“环境电场—管线电位数据库”。然后利用虚拟仪器技术开发了海底管线环境电场分布电位实时测量系统,通过水下机器人携带该测量系统,沿管线方向现场测量海底管线周围海水的电位分布,进而通过“环境电场——管线电位数据库”反演获得海底管线表面的保护电位和保护电流密度分布,从而准确实施海底管线的涂层破损点检测。最后论文根据实海环境数据和材料腐蚀数据,利用BP人工神经网络模型建立了金属管线在海水环境中腐蚀速度与环境因素之间的神经网络预测模型,实现了非线性预测不同海水环境中金属管线的腐蚀速度。
数值仿真计算部分以边界元方法(BEM)为基础,对腐蚀电场仿真模型进行了推导和计算,该模型可以计算阴极保护下管线的电位和电流密度分布。论文应用Matlab开发出阴极保护下腐蚀电场仿真计算软件,根据仿真计算结果建立“环境电场—管线电位数据库”,并且通过算例进行了验证,结果表明本论文所开发的仿真计算软件是有效和可行的,为实现不接触测量管线电位奠定重要基础。
不接触测量部分根据海底管线周围海水中电场分布电位与管线腐蚀状态有显著相关性,通过海水中电场电位测量和处理可以得到管线的腐蚀状态。在深水状况下,直接测量管道的电极电位具有相当大的困难,而海底管道附近海水中电位分布有其自身的特点,在距海底管道不同位置处电位值不一样,通过测定海水中电位差,查询“环境电场—管线电位数据库”可得到海底管道的表面分布电位,从而获得管道腐蚀状态。这部分主要内容是建立以软件为中心的虚拟仪器测量系统,首先研制了深海用全固态银/氯化银电极作为参比电极,并组建成信号测量探头,对电极的性能进行了全面测试;数据采集和信号处理采用船载一体化工作站进行;最后应用Labview编制实时测量软件。测量系统和ROV系统集成后对阴极保护下海水环境电场进行自动测量,将测量结果进行反演,从而实现不接触测量阴极保护下管线表面电位。分别在青岛滨海进行了模拟实验和渤海实海检测,测试结果表明该系统在距离海底管线4米内均可以准确测量其周围的阴极保护电场,检测出管线表面涂层破损点。
海底管线腐蚀速度预测部分是应用人工神经网络技术于腐蚀研究领域,将管线钢与海水环境因素作为共同影响腐蚀速度的因素,利用BP人工神经网络算法建立海水环境因素与管线钢腐蚀速度之间的预测模型。预测软件采用LabVIEW中调用MATLABScript节点的方式,利用MATLAB强大的神经元工具箱完成对核心程序的设计,该预测系统可用于非线性预测管线钢在不同海域的腐蚀速度,又可以不受水深的局限,因此在海洋腐蚀与防护领域的推广应用将是大势所趋,是潜水、机器人检测或自动监测等手段所无法比拟的。
本文研究的海底管道不接触测量系统和腐蚀速度预测系统能实现对海底管线阴极保护系统的有效性进行全程实时监测和长期的腐蚀速度预测,具有很大的实用价值。这将有利于全面了解海底管道的运行状态和腐蚀情况,及时发现影响管道安全运行的隐患以便使之得到及时的修复。可为海洋油气的安全运输和科学管理,以及进一步改进和完善管线的防蚀系统设计提供基础数据和决策依据。
This research project comes from the State Hi-Tech Research and Development Plan (863) "Submarine Pipeline’s Exterior Detection Devices and Detection Technology. "(2004AA602210)
The marine environment is an extremely corrosive environment. The oil and gas transmission pipelines’s corrosion in the marine environment are more serious than that on land. Physical, chemical and biological factors all could lead to corrosion of the submarine pipeline. Once the submarine pipeline corroded, it may be very difficult and expensive to maintain. Corrosion of the submarine pipeline is a long and slow process. With the increase of pipeline’s service time, is the pipeline corroded? Can sacrificial anode work in a normal state? Does cathodic protection current density meet the design requirements? All these results will affect the integrity of pipeline during its design life. To study on the pipeline’s corrosion features in the marine environment, real-time detect pipeline corrosion, and predict pipeline’s corrosion rate, which is important to the design, operation and maintenance of pipeline cathodic protection system.
In this paper, a non-contact detection method was the first time put forword for the pipeline with cathodic protection and a non-linear predictive model for pipeline corrosion was established. The study is divided into three main parts, including simulation calculation of cathode protection field. non-contact measurement of pipeline potential and corrosion rate prediction of pipeline. Firstly, boundary element method was used to calculate the potential distribution of corrosion electric field according to the anodes distribution on pipelines, and a database of environmental field and pipeline potential was established. Based on the virtual instrument technology, environment potential distribution measurement system was developed, which was carried by a ROV to measure the electric filed distribution along the pipeline, then the surface protection potential and current density were inversed by the above database, and then to fulfill pipelines damage points detection. Finally, based on the BP neural network model, a neural network prediction model in the marine environment was established. This model can be used to predict the rate of corrosion of pipelines.
Numerical calculation study was based on the boundary element method (BEM). The corrosion electric field can be calculated. Matlab was used to calculate the potential distribution of corrosion electric field, and a database of environmental field to pipeline potential was established according to the calculated results. It through some examples showed that the simulation software was effective and feasible, laid an important foundation for non-contact measurement pipelines.
Non-contact measurement was based on the significant correlation between the surrounding potential of seabed pipelines and pipeline corrosion. Through potential measure and data processing, pipeline corrosion can be known. The all-solid-state hardware AgCl electrodes used as the reference electrode. Ship-take workstation with integrated data acquisition card was used to acquire data and process signal. Labview software was used to develop real-time measurement software. Measurement results was inversed to get the pipeline surface potential, thus non-contact measurement cathodic protection potential was realised. Simulation tests in Qingdao coastal and field tests in Bohai Sea were done and the test results showed that the system within four meters to the pipeline could accurately measure electric field around the cathodic protected pipeline and detect its surface damage.
Submarine Pipeline corrosion rate prediction is based on artificial neural network technology. Pipeline and the marine environment as a common factor affecting the rate of corrosion, BP artificial neural network algorithms was used to establish marine environment and the pipeline steel corrosion rate prediction model. Forecast software Calling MATLABScript nodes. MATLAB toolbox neurons completed the design of the core. The forecasting system can be used to predict the corrosion rate of steel pipe in the sea. Therefore, the application in the field of marine corrosion and protection is a general trend.
Study on non-contact measurement system and submarine pipeline corrosion rate prediction system.Real-time monitoring of the effectiveness and long-term corruption corrosion rate prediction can be realized for the pipeline cathodic protection system, which has great value to know submarine pipeline operations and corrosion, and find some hidden danger to repair in time. It will provide basic data and basis for decision-making for the safe transportation and scientific management of marine oil and gas, and improvement of the pipeline’s anti-corrosion system.
海洋是一个腐蚀性极强的环境,各种钢铁设施在海洋环境中极易发生腐蚀破坏。在海洋环境下传输油气的金属管线腐蚀远比陆地上要严重,物理因素、化学因素和生物因素均可导致管线的腐蚀,而且一旦发生腐蚀破坏,维修极为困难,后果不堪设想。由于海底管线的腐蚀是一种长期缓慢的过程,随着管线服役时间的增加,管线的腐蚀状态如何?牺牲阳极能否处于正常的工作状态?能否达到设计的保护电流密度?其结果都将严重影响管线在设计年限的正常运转。因此研究海水环境中常用金属管线的腐蚀特征、实时检测管线腐蚀状态、预测管线腐蚀速度,对管线的阴极保护系统的设计、运行维护都有重要的意义。尽管目前我国还没有水深超过200米的海底石油天然气管线,随着油气开采范围的拓展,深海油气传输将成为现实问题,所以深海管线腐蚀检测方法的研究,无论技术上还是在理论上都具有具有重要意义。
本论文首次提出一种非接触式检测阴极保护下海底管线腐蚀状态的方法和非线性预测海底管线腐蚀速度的模型。主要研究内容分为海底管线阴极保护电场仿真计算、不接触测量管线电位和海底管线腐蚀速度预测三部分。论文首先应用边界元理论仿真计算管线周围环境电场和管线表面的分布电位,建立相应的“环境电场—管线电位数据库”。然后利用虚拟仪器技术开发了海底管线环境电场分布电位实时测量系统,通过水下机器人携带该测量系统,沿管线方向现场测量海底管线周围海水的电位分布,进而通过“环境电场——管线电位数据库”反演获得海底管线表面的保护电位和保护电流密度分布,从而准确实施海底管线的涂层破损点检测。最后论文根据实海环境数据和材料腐蚀数据,利用BP人工神经网络模型建立了金属管线在海水环境中腐蚀速度与环境因素之间的神经网络预测模型,实现了非线性预测不同海水环境中金属管线的腐蚀速度。
数值仿真计算部分以边界元方法(BEM)为基础,对腐蚀电场仿真模型进行了推导和计算,该模型可以计算阴极保护下管线的电位和电流密度分布。论文应用Matlab开发出阴极保护下腐蚀电场仿真计算软件,根据仿真计算结果建立“环境电场—管线电位数据库”,并且通过算例进行了验证,结果表明本论文所开发的仿真计算软件是有效和可行的,为实现不接触测量管线电位奠定重要基础。
不接触测量部分根据海底管线周围海水中电场分布电位与管线腐蚀状态有显著相关性,通过海水中电场电位测量和处理可以得到管线的腐蚀状态。在深水状况下,直接测量管道的电极电位具有相当大的困难,而海底管道附近海水中电位分布有其自身的特点,在距海底管道不同位置处电位值不一样,通过测定海水中电位差,查询“环境电场—管线电位数据库”可得到海底管道的表面分布电位,从而获得管道腐蚀状态。这部分主要内容是建立以软件为中心的虚拟仪器测量系统,首先研制了深海用全固态银/氯化银电极作为参比电极,并组建成信号测量探头,对电极的性能进行了全面测试;数据采集和信号处理采用船载一体化工作站进行;最后应用Labview编制实时测量软件。测量系统和ROV系统集成后对阴极保护下海水环境电场进行自动测量,将测量结果进行反演,从而实现不接触测量阴极保护下管线表面电位。分别在青岛滨海进行了模拟实验和渤海实海检测,测试结果表明该系统在距离海底管线4米内均可以准确测量其周围的阴极保护电场,检测出管线表面涂层破损点。
海底管线腐蚀速度预测部分是应用人工神经网络技术于腐蚀研究领域,将管线钢与海水环境因素作为共同影响腐蚀速度的因素,利用BP人工神经网络算法建立海水环境因素与管线钢腐蚀速度之间的预测模型。预测软件采用LabVIEW中调用MATLABScript节点的方式,利用MATLAB强大的神经元工具箱完成对核心程序的设计,该预测系统可用于非线性预测管线钢在不同海域的腐蚀速度,又可以不受水深的局限,因此在海洋腐蚀与防护领域的推广应用将是大势所趋,是潜水、机器人检测或自动监测等手段所无法比拟的。
本文研究的海底管道不接触测量系统和腐蚀速度预测系统能实现对海底管线阴极保护系统的有效性进行全程实时监测和长期的腐蚀速度预测,具有很大的实用价值。这将有利于全面了解海底管道的运行状态和腐蚀情况,及时发现影响管道安全运行的隐患以便使之得到及时的修复。可为海洋油气的安全运输和科学管理,以及进一步改进和完善管线的防蚀系统设计提供基础数据和决策依据。
This research project comes from the State Hi-Tech Research and Development Plan (863) "Submarine Pipeline’s Exterior Detection Devices and Detection Technology. "(2004AA602210)
The marine environment is an extremely corrosive environment. The oil and gas transmission pipelines’s corrosion in the marine environment are more serious than that on land. Physical, chemical and biological factors all could lead to corrosion of the submarine pipeline. Once the submarine pipeline corroded, it may be very difficult and expensive to maintain. Corrosion of the submarine pipeline is a long and slow process. With the increase of pipeline’s service time, is the pipeline corroded? Can sacrificial anode work in a normal state? Does cathodic protection current density meet the design requirements? All these results will affect the integrity of pipeline during its design life. To study on the pipeline’s corrosion features in the marine environment, real-time detect pipeline corrosion, and predict pipeline’s corrosion rate, which is important to the design, operation and maintenance of pipeline cathodic protection system.
In this paper, a non-contact detection method was the first time put forword for the pipeline with cathodic protection and a non-linear predictive model for pipeline corrosion was established. The study is divided into three main parts, including simulation calculation of cathode protection field. non-contact measurement of pipeline potential and corrosion rate prediction of pipeline. Firstly, boundary element method was used to calculate the potential distribution of corrosion electric field according to the anodes distribution on pipelines, and a database of environmental field and pipeline potential was established. Based on the virtual instrument technology, environment potential distribution measurement system was developed, which was carried by a ROV to measure the electric filed distribution along the pipeline, then the surface protection potential and current density were inversed by the above database, and then to fulfill pipelines damage points detection. Finally, based on the BP neural network model, a neural network prediction model in the marine environment was established. This model can be used to predict the rate of corrosion of pipelines.
Numerical calculation study was based on the boundary element method (BEM). The corrosion electric field can be calculated. Matlab was used to calculate the potential distribution of corrosion electric field, and a database of environmental field to pipeline potential was established according to the calculated results. It through some examples showed that the simulation software was effective and feasible, laid an important foundation for non-contact measurement pipelines.
Non-contact measurement was based on the significant correlation between the surrounding potential of seabed pipelines and pipeline corrosion. Through potential measure and data processing, pipeline corrosion can be known. The all-solid-state hardware AgCl electrodes used as the reference electrode. Ship-take workstation with integrated data acquisition card was used to acquire data and process signal. Labview software was used to develop real-time measurement software. Measurement results was inversed to get the pipeline surface potential, thus non-contact measurement cathodic protection potential was realised. Simulation tests in Qingdao coastal and field tests in Bohai Sea were done and the test results showed that the system within four meters to the pipeline could accurately measure electric field around the cathodic protected pipeline and detect its surface damage.
Submarine Pipeline corrosion rate prediction is based on artificial neural network technology. Pipeline and the marine environment as a common factor affecting the rate of corrosion, BP artificial neural network algorithms was used to establish marine environment and the pipeline steel corrosion rate prediction model. Forecast software Calling MATLABScript nodes. MATLAB toolbox neurons completed the design of the core. The forecasting system can be used to predict the corrosion rate of steel pipe in the sea. Therefore, the application in the field of marine corrosion and protection is a general trend.
Study on non-contact measurement system and submarine pipeline corrosion rate prediction system.Real-time monitoring of the effectiveness and long-term corruption corrosion rate prediction can be realized for the pipeline cathodic protection system, which has great value to know submarine pipeline operations and corrosion, and find some hidden danger to repair in time. It will provide basic data and basis for decision-making for the safe transportation and scientific management of marine oil and gas, and improvement of the pipeline’s anti-corrosion system.
引文
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