利用人工神经网络技术预测气田环境下316L不锈钢临界点蚀温度

doi:10.11902/1005.4537.2015.122

中国腐蚀与防护学报

2016, Vol. 36

Issue (3): 205-211 DOI: 10.11902/1005.4537.2015.122

研究报告

本期目录 | 过刊浏览

利用人工神经网络技术预测气田环境下316L不锈钢临界点蚀温度

刘静¹,李晓禄¹,朱崇伟¹,张涛^1,²(

),曾冠鑫³,孟国哲^1,²,邵亚薇^1,²

1. 哈尔滨工程大学材料科学与化学工程学院哈尔滨 150001
2. 中国科学院金属研究所沈阳 110016
3. 塔里木油田分公司塔北勘探开发项目经理部地面部库尔勒 841000

Prediction of Critical Pitting Temperature of 316L Stainless Steel in Gas Field Environments by Artificial Neutral Network

Jing LIU¹,Xiaolu LI¹,Chongwei ZHU¹,Tao ZHANG^1,²(

),Guanxin ZENG³,Guozhe MENG^1,²,Yawei SHAO^1,²

1. College of Materials Science and Chemical Engineering, Harbin Engineering University, Harbin 150001, China
2. Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China
3. North-Tarim Management Department of Exploration and Development Project, Petrochina Tarim Oilfield Company, Korla 841000, China

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摘要:

根据气田现场工况的调研情况,应用动电位极化法测量了典型气田环境中316L不锈钢的临界点蚀温度 (CPT),并利用人工神经网络 (ANN) 技术对CPT进行了预测。结果表明,CPT随Cl^-浓度升高而降低,pH值对CPT影响很小。建立的ANN模型对316L不锈钢的CPT具有良好的预测能力,可实现对气田各作业区复杂耦合环境下CPT的预测。ANN模型的预测结果表明,Cl^-浓度和pH值对CPT的影响无交互作用,Cl^-浓度是影响CPT的主要因素,因此Cl^-将是气田防腐蚀工程的重点控制因素。

关键词 ： 316L不锈钢, 气田环境, 临界点蚀温度, 人工神经网络, 腐蚀预测

Abstract：

316L stainless steel is widely used for enhancing the pitting resistance of pipelines in gas field. The corrosion environment is complex and diversified in different working districts of gas field. Therefore, it is necessary to develop a model for predicting the pitting resistance of pipelines serving in different corrosive environments. Critical pitting temperature (CPT) is considered as a criterion for evaluating the pitting resistance of stainless steel. Based on a survey on the operation situations of gas field, the relevant data of CPT for 316L stainless steel is acquired by potentiodynamic polarization method in solutions with various Cl^- concentrations and pH values, which are selected to correspond with the real environments in operation. Then, an artificial neutral network (ANN) model is developed to predict the CPT, and therewith to compare with the measured data. The results show that the CPT decreases with the increase of Cl^- concentration, but on which pH value has little influence. The developed ANN model has good ability to predict the CPT of 316L stainless steel, and can be used for the prediction of CPT in complex environments in gas field. It is also revealed that there is no interactive effect between Cl^- concentration and pH value, and Cl^- concentration was the main influencing factor on the CPT. Therefore, Cl^- concentration will be peculiarly concerned with for the implementation of a corrosion control project in gas field.

Key words： 316L stainless steel gas field critical pitting temperature artificial neutralnetwork prediction

收稿日期: 2015-09-15

基金资助:国家自然科学基金项目 (U1460202) 资助

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引用本文:

刘静,李晓禄,朱崇伟,张涛,曾冠鑫,孟国哲,邵亚薇. 利用人工神经网络技术预测气田环境下316L不锈钢临界点蚀温度[J]. 中国腐蚀与防护学报, 2016, 36(3): 205-211.
Jing LIU, Xiaolu LI, Chongwei ZHU, Tao ZHANG, Guanxin ZENG, Guozhe MENG, Yawei SHAO. Prediction of Critical Pitting Temperature of 316L Stainless Steel in Gas Field Environments by Artificial Neutral Network. Journal of Chinese Society for Corrosion and protection, 2016, 36(3): 205-211.

链接本文:

https://www.jcscp.org/CN/10.11902/1005.4537.2015.122 或 https://www.jcscp.org/CN/Y2016/V36/I3/205

图1 工作电极示意图[11]

图2 新疆某气田各作业区地层水中Cl-浓度及pH值分布的累积概率

图3 人工神经网络结构示意图

图4 Cl-浓度为1.4×105 mg/L,pH=4时不同温度下316L不锈钢的动电位极化曲线及击破电位随温度的变化规律

图5 不同Cl-浓度和pH值条件下316L不锈钢击破电位随温度变化曲线

表1 不同Cl-浓度和pH值条件下316L不锈钢的CPT

图6 316L不锈钢CPT随Cl-浓度变化规律

图7 不同pH值条件下316L不锈钢的CPT

表2 CPT预测值与实验值对比表

图8 CPT预测值与实验值对比图

图9 CPT随NaCl浓度和pH值变化的三维曲面

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