气田集输管线的腐蚀预测与防护技术研究

doi:10.11902/1005.4537.2025.340

中国腐蚀与防护学报 DOI: 10.11902/1005.4537.2025.340

本期目录 | 过刊浏览

气田集输管线的腐蚀预测与防护技术研究

谭军¹,崔春江¹,段明霞²,刘祥周³,孟海龙¹,蒋瑛¹

1.中国石油长庆油田分公司第三采气厂采气工艺研究所鄂尔多斯 017399

2.中国石油长庆油田分公司培训中心西安 710021

Corrosion Prediction and Prevention for Gathering Pipelines in Gas Field

1. The Third Gas Production Plant of Petro China Changqing Oilfield Company Gas Production Technology Research Institute, Erdos City 0 17399, China

2. Training Center of PetroChina Changqing Oilfield Company, Erdos City 017399, China

引用本文:

谭军崔春江段明霞刘祥周孟海龙蒋瑛. 气田集输管线的腐蚀预测与防护技术研究[J]. 中国腐蚀与防护学报, 10.11902/1005.4537.2025.340.

全文: PDF(1391 KB)

摘要: 气田某区块集输管线的腐蚀风险突出。为此，本研究构建了涵盖腐蚀因素识别、腐蚀速率预测与防控策略优化的综合技术体系。通过模拟该区块集输管线腐蚀环境，采用正交实验结合熵权-秩和比法，揭示了CO2分压、Cl-浓度和SRB浓度为均匀腐蚀和局部腐蚀的主控因素，但对两类腐蚀的影响权重有差异。针对该腐蚀特征，将De Waard95 CO2腐蚀模型进行了Cl-浓度与微生物浓度的引入与修正，显著提升了模型预测精度，验证决定系数R2达80.98%。该研究还优选出OED/BKC复配缓蚀杀菌剂，在模拟干管条件下实现了均匀缓蚀效率达94.25%，点蚀缓蚀率达90.74%，灭菌率接近100%。研究成果为该区块建立腐蚀预警与防护的成套技术提供了有效支撑。

关键词 ：气田, 集输管线, 腐蚀权重, 腐蚀预测, 缓蚀杀菌剂

Abstract：The gathering pipelines of a gas field in the Northwestern region suffered from severe corrosion risk, hence a comprehensive technical countermeasure was then postulated covering the identification of corrosion factors, the prediction of corrosion-rate, and the optimization of prevention strategies. Herein, the corrosion behavior of the gathering pipeline steel was assessed via a simulation set of the corrosive environment for the pipelines of the gas field in this region, as well as an orthogonal experimental arrangement combined with the entropy-weight rank-sum ratio method. It was revealed that the CO₂ partial pressure, the chloride concentration, and the SRB activity were the main controlling factors for uniform corrosion and local corrosion, but the influence weights of these factors on the two types of corrosion were different. Further, a modified De Waard95 CO₂ corrosion model was developed by introducing correction terms for Cl^- concentration and microbial activity, which significantly improved the corrosion-rate prediction accuracy and achieved a determination coefficient (R²) of 80.98%. Considering the main corrosion factors in the gas field, an OED/BKC composite inhibiting biocide system was developed. In the simulated conditions, the corresponding inhibition rate for uniform corrosion reached 94.25% and that for pitting corrosion was 90.74% at optimal concentrations of 150 mg/L OED and 50 mg/L BKC, with nearly 100% effectiveness for sterilization. The proposed methodology provides a reliable technical basis for corrosion warning and prevention in this gas field.

Key words： Gas Filed Gathering Pipeline Corrosion Factor Weight Corrosion Prediction Inhibiting Biocide

收稿日期: 2025-10-30

ZTFLH:

TE832

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	谭军
	崔春江
	段明霞
	刘祥周
	孟海龙
	蒋瑛
44.8K

链接本文:

https://www.jcscp.org/CN/Y0/V/I/0

[1]	张雄斌, 党恩, 于晓婧, 汤玉斐, 赵康. 油气田用马氏体不锈钢腐蚀性能研究现状与进展[J]. 中国腐蚀与防护学报, 2025, 45(4): 837-848.
[2]	张明, 龚宁, 张博, 霍宏博, 李浩男, 钟显康. 基于腐蚀预测和强度计算的油管服役寿命评估[J]. 中国腐蚀与防护学报, 2024, 44(3): 781-788.
[3]	朱烨森, 蔡锟, 胡葆文, 夏云秋, 胡涛勇, 黄一. 海底管道CO₂ 腐蚀特性及预测模型研究进展[J]. 中国腐蚀与防护学报, 2023, 43(6): 1225-1236.
[4]	王莎莎, 马帅杰, 车琨, 杜艳霞. 机器学习在自然环境腐蚀评估与预测领域的应用现状[J]. 中国腐蚀与防护学报, 2023, 43(3): 441-451.
[5]	王炳钦, 张晓莲, 雍兴跃, 周欢, 高新华. 舰船海水管系中紫铜/钢制管道耦接后电偶腐蚀的数值模拟研究[J]. 中国腐蚀与防护学报, 2022, 42(2): 200-210.
[6]	李洋, 李承媛, 陈旭, 杨佳星, 王欣彤, 明男希, 韩镇泽. 超级13Cr不锈钢在海洋油气田环境中腐蚀行为灰关联分析[J]. 中国腐蚀与防护学报, 2018, 38(5): 471-477.
[7]	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.
[8]	王凤平,刘岚,丁言伟,胡隋军,刘照斌,刘丹,张丽. 油气田H₂S腐蚀体系模拟方法探究[J]. 中国腐蚀与防护学报, 2015, 35(3): 251-256.
[9]	韩夏冰高志明党丽华王迎毕慧超. Q235钢在模拟大气环境中早期腐蚀图像小波包分析[J]. 中国腐蚀与防护学报, 2013, 33(3): 211-215.
[10]	张国安; 陈长风; 路民旭; 吴荫顺 . 油气田中CO2腐蚀的预测模型[J]. 中国腐蚀与防护学报, 2005, 25(2): 119-123 .
[11]	谭晓明 . 基于人工神经网络的铝合金腐蚀预测及其精度分析[J]. 中国腐蚀与防护学报, 2004, 24(4): 218-221 .
[12]	崔大为; 林乐耘; 赵月红 . 应用人工神经网络预测有色金属海水腐蚀的长期行为[J]. 中国腐蚀与防护学报, 2004, 24(1): 29-32 .
[13]	张世超; 白致铭; 吴志龙 . 酰胺基烷基膦酸类缓蚀杀菌剂在油田注水环境中缓蚀、杀菌性能研究[J]. 中国腐蚀与防护学报, 2003, 23(4): 231-233 .
[14]	蔡建平;柯伟. 应用人工神经网络预测碳钢、低合金钢的大气腐蚀[J]. 中国腐蚀与防护学报, 1997, 17(4): 303-306.

Viewed

Full text

Abstract

Cited

Shared

Discussed