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喻西崇, 赵金洲, 纪录军, 胡永全. 利用神经网络分析注水管道内腐蚀影响因素[J]. 油气储运, 2003, 22(2): 27-31. DOI: 10.6047/j.issn.1000-8241.2003.02.007
引用本文: 喻西崇, 赵金洲, 纪录军, 胡永全. 利用神经网络分析注水管道内腐蚀影响因素[J]. 油气储运, 2003, 22(2): 27-31. DOI: 10.6047/j.issn.1000-8241.2003.02.007
shu
YU Xichong, ZHAO Jinzhou, . Corrosion Influence Factors Analysis Using Neural Network for Injecting Pipeline[J]. Oil & Gas Storage and Transportation, 2003, 22(2): 27-31. DOI: 10.6047/j.issn.1000-8241.2003.02.007
Citation: YU Xichong, ZHAO Jinzhou, . Corrosion Influence Factors Analysis Using Neural Network for Injecting Pipeline[J]. Oil & Gas Storage and Transportation, 2003, 22(2): 27-31. DOI: 10.6047/j.issn.1000-8241.2003.02.007
shu

利用神经网络分析注水管道内腐蚀影响因素

Corrosion Influence Factors Analysis Using Neural Network for Injecting Pipeline

    摘要
  • 摘要: 对注水管道内两种腐蚀影响因素进行了排序, 采用灰色关联分析法和二层BP神经网络法对主要影响因素进行了考察。示例分析表明, 采用改进二层BP神经网络得到的连接权值排序, 比灰色关联法更能准确反映注水管道内腐蚀实际情况。根据某注水试验区注水管道水质分析数据及计算结果, 得出其主要影响因素的高低排序为, 溶解氧(0.877), pH值(0.856), 硫酸盐还原菌(0.84), 温度(0.811), 压力(0.78), CO2(0.76), 流速(0.736)。

     

    Abstract: Two methods are put forward to sort for injecting pipeline corrosion influence factors and to determine main influence factors, namely gray relation analysis and two layers BP neural network in this paper.The field examples show that prediction results of two layers BP neural network are better than those of gray relation analysis.Therefore, two layers BP neural network method should be adopted to analyze injecting pipeline corrosion influence factors to determine main influence factor.In this experimental zones, main influence factor of corrosion are sorted as below: O2 (0.877))pH (0.856)>SRB (0.84) >temperature(0.811)> pressure (0.78) >CO2 (0.76)>flow velocity (0.736)>0.7.

     

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