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A Numerical Corrosion Rate Prediction Method for Direct Assessment of Wet Gas Gathering Pipelines Internal Corrosion

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  • Kexi Liao

    (School of Petroleum Engineering, Southwest Petroleum University, Chengdu 610500, China
    Key Laboratory of Oil & Gas Storage and Transportation, Southwest Petroleum University, Chengdu 610500, China)

  • Quanke Yao

    (China Petroleum Engineering Co. Ltd., Southwest Company, Chengdu 610500, China)

  • Xia Wu

    (School of Petroleum Engineering, Southwest Petroleum University, Chengdu 610500, China
    Key Laboratory of Oil & Gas Storage and Transportation, Southwest Petroleum University, Chengdu 610500, China)

  • Wenlong Jia

    (School of Petroleum Engineering, Southwest Petroleum University, Chengdu 610500, China
    Key Laboratory of Oil & Gas Storage and Transportation, Southwest Petroleum University, Chengdu 610500, China)

Abstract

The paper introduces a numerical internal corrosion rate prediction method into the internal corrosion direct assessment (ICDA) process for wet gas gathering pipelines based on the back propagation (BP), the genetic algorithm (GA) and BP, and the particle swarm optimization and BP artificial neural networks (ANNs). The basic data were collected in accordance with the terms established by the National Association of Corrosion Engineers in the Wet Gas Internal Corrosion Direct Assessment (WG-ICDA) SP0110, and the corrosion influencing factors, which are the input variables of the ANN model, are identified and refined by the grey relational analysis method. A total of 116 groups of basic data and inspection data from seven gathering pipelines in Sichuan (China) are used to develop the numerical prediction model. Ninety-five of the 116 groups of data are selected to train the neural network. The remaining 21 groups of data are chosen to test the three ANNs. The test results show that the GA and BP ANN yield the smallest number of absolute errors and should be selected as the preferred model for the prediction of corrosion rates. The accuracy of the model was validated by another 54 groups of excavation data obtained from pipeline No. 8, whose internal environment parameters are similar to those found in the training and testing pipelines. The results show that the numerical method yields significantly better absolute errors than either the de Waard 95 model or the Top-of-Line corrosion model in WG-ICDA when applying the approach to specific pipelines, and it can be used to investigate a specific pipeline for which the data have been collected and the ANN has been developed in WG-ICDA SP0110.

Suggested Citation

  • Kexi Liao & Quanke Yao & Xia Wu & Wenlong Jia, 2012. "A Numerical Corrosion Rate Prediction Method for Direct Assessment of Wet Gas Gathering Pipelines Internal Corrosion," Energies, MDPI, vol. 5(10), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:10:p:3892-3907:d:20625
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    Cited by:

    1. Kimiya Zakikhani & Fuzhan Nasiri & Tarek Zayed, 2021. "A failure prediction model for corrosion in gas transmission pipelines," Journal of Risk and Reliability, , vol. 235(3), pages 374-390, June.
    2. María E. Arce & Ángeles Saavedra & José L. Míguez & Enrique Granada & Antón Cacabelos, 2013. "Biomass Fuel and Combustion Conditions Selection in a Fixed Bed Combustor," Energies, MDPI, vol. 6(11), pages 1-17, November.
    3. Alexey Dengaev & Vladimir Verbitsky & Olga Eremenko & Anna Novikova & Andrey Getalov & Boris Sargin, 2022. "Water-in-Oil Emulsions Separation Using a Controlled Multi-Frequency Acoustic Field at an Operating Facility," Energies, MDPI, vol. 15(17), pages 1-16, August.
    4. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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