Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Haotian Li, Kun Huang, Qin Zeng and Chong Sun
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Haotian Li: School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China
Kun Huang: School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China
Qin Zeng: PetroChina Southwest Oil and Gas Field Gas Branch, Chengdu 610500, China
Chong Sun: Sinopec Petroleum Engineering Zhongyuan Corporation, Puyang 457000, China

Energies, 2022, vol. 15, issue 3, 1-30

Abstract: Prediction of residual strength and residual life of corrosion pipelines is the key to ensuring pipeline safety. Accurate assessment and prediction make it possible to prevent unnecessary accidents and casualties, and avoid the waste of resources caused by the large-scale replacement of pipelines. However, due to many factors affecting pipeline corrosion, it is difficult to achieve accurate predictions. This paper reviews the research on residual strength and residual life of pipelines in the past decade. Through careful reading, this paper compared several traditional evaluation methods horizontally, extracted 71 intelligent models, discussed the publishing time, the evaluation accuracy of traditional models, and the prediction accuracy of intelligent models, input variables, and output value. This paper’s main contributions and findings are as follows: (1) Comparing several traditional evaluation methods, PCORRC and DNV-RP-F101 perform well in evaluating low-strength pipelines, and DNV-RP-F101 has a better performance in evaluating medium–high strength pipelines. (2) In intelligent models, the most frequently used error indicators are mean square error, goodness of fit, mean absolute percentage error, root mean square error, and mean absolute error. Among them, mean absolute percentage error was in the range of 0.0123–0.1499. Goodness of fit was in the range of 0.619–0.999. (3) The size of the data set of different models and the data division ratio was counted. The proportion of the test data set was between 0.015 and 0.4. (4) The input variables and output value of predictions were summarized.

Keywords: residual strength; residual life; evaluation criterion; intelligent model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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