喻西崇, 赵金洲, 邬亚玲, 胡永全, 纪禄军. 注水管道腐蚀缺陷裂纹扩展速率的确定方法[J]. 油气储运, 2003, 22(3): 26-29.
引用本文: 喻西崇, 赵金洲, 邬亚玲, 胡永全, 纪禄军. 注水管道腐蚀缺陷裂纹扩展速率的确定方法[J]. 油气储运, 2003, 22(3): 26-29.
YU Xichong, ZHAO Jinzhou, . Corrosion Influence Factors Analysis Using Neural Network for Injecting Pipeline[J]. Oil & Gas Storage and Transportation, 2003, 22(3): 26-29.
Citation: YU Xichong, ZHAO Jinzhou, . Corrosion Influence Factors Analysis Using Neural Network for Injecting Pipeline[J]. Oil & Gas Storage and Transportation, 2003, 22(3): 26-29.

注水管道腐蚀缺陷裂纹扩展速率的确定方法

Corrosion Influence Factors Analysis Using Neural Network for Injecting Pipeline

  • 摘要: 在试验的基础上,研究了注水管道中以腐蚀为主、疲劳为辅的腐蚀缺陷裂纹扩展机理,认为腐蚀缺陷裂纹扩展主要是由钝化膜开裂引起的。在此基础上,建立了腐蚀缺陷裂纹扩展速率的数学模型,并提出了求解此数学模型的有效方法。以胜利油田营11试验区某段加缓蚀剂的注水管道为例,利用腐蚀缺陷疲劳裂纹扩展数学模型预测注水管道腐蚀缺陷尺寸随时间的变化趋势,预测结果和智能检测仪的检测结果基本一致。

     

    Abstract: In this paper, based on experiment, corrosion crack extension mechanism in injecting water pipeline where corrosion is main and fatigue is second is studied, namely corrosion fatigue crack extension is mainly caused by passive film cracking. On the basis of the mechanism, mathematical model of corrosion fatigue crack extension velocity is established and effective methods of solving the model are gained. Take the given injecting water pipeline that is added into inhibition corrosion for YING-11 experimental zones of Shengli Oilfield as an example, the corrosion fatigue crack extension mathematical model established in this article is used to predict corrosion fatigue defect sizes varying propensity with time, and is verified that prediction values are good agreement with field test value, which shows that the mathematical model established in this paper is valid right.

     

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