Uniform corrosion assessment in oil and gas pipelines using corrosion prediction models – Part 1: models performance and limitations for operational field cases

Cruz, João Pedro Bachega; Veruz, Edilson Gabriel; Aoki, Idalina Vieira; Schleder, Adriana Miralles; Souza, Gilberto Francisco Martha de; Vaz, Gustavo L.; Barros, Leonardo O. de; Orlowski, Rene Thiago C.; Martins, Marcelo Ramos

doi:10.1016/j.psep.2022.09.034

Cited by 15 publications

(1 citation statement)

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“…Determining accurate corrosion rates can aid in accurately predicting the failure of natural gas pipelines. Moreover, it can provide scientific guidance for both decision making and corrosion risk management regarding natural gas pipelines [4]. Many gas fields in China and around the world are implementing pipeline and even lead to major safety accidents [2,3].…”

Section: Introductionmentioning

confidence: 99%

Research and Application for Corrosion Rate Prediction of Natural Gas Pipelines Based on a Novel Hybrid Machine Learning Approach

Yu²,

Zhu³

et al. 2023

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An accurate and stable prediction of the corrosion rate of natural gas pipelines has a major impact on pipeline material selection, inhibitor filling process, and maintenance schedules. At present, corrosion data are impacted by non-linearity and noise interference. The traditional corrosion rate prediction methods often ignore noise data, and only a small number of researchers have carried out in-depth research on non-linear data processing. Therefore, an innovative hybrid prediction model has been proposed with four processes: data preprocessing, optimization, prediction, and evaluation. In the proposed model, a decomposing algorithm is applied to eliminate redundant noise and to extract the primary characteristics of the corrosion data. Stratified sampling is applied to separate the training set and the test set to avoid deviation due to the sampling randomness of small samples. An improved particle swarm optimization algorithm is applied to optimize the parameters of support vector regression. A comprehensive evaluation of this framework is also conducted. For natural gas pipelines in southwest China, the coefficient of determination and mean absolute percentage error of the proposed hybrid model are 0.925 and 5.73%, respectively, with better prediction performance compared to state-of-the-art models. The results demonstrate the best approach for improving the prediction accuracy of the proposed hybrid model. This can be applied to improve the corrosion control effect and to support the digital transformation of the corrosion industry.

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Section: Introductionmentioning

confidence: 99%