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<p>Pipelines in the oil and gas industry are susceptible to severe corrosion due to harsh operating conditions they encounter. Properly projecting corrosion is therefore required to render the pipelines safe and dependable. While machine learning (ML) techniques have been more commonly applied to corrosion projection, there remain certain challenges in optimizing model performance, handling data complexity, and improving interpretability for industrial purposes. Filling this gap, in the present work an attempt was made to dig deeper and develop a robust projection framework with hybrid ML schemes that are employed with improved corrosion forecast precision. Systematic discussion of diverse algorithms created to project corrosion in pipelines that are an integral part of the transmission of oil and natural gas has been illustrated. Various machine learning scenarios like SVR and AdaBoost have been employed individually and synergistically through studies. The comparison began with traditional schemes followed by hybrid schemes, using a suite of statistical metrics for comparison and validation of the schemes. The outcome was that while hybrid AdaBoost schemes had superior training capacity, SVR schemes had superior projection capabilities for pipeline corrosion, especially optimized by SMA (with R<sup>2</sup> = 0.98687). In comparison to the traditional schemes, the hybrid schemes demonstrated considerable improvements in accuracy and reliability, efficiently reducing projection errors and enhancing the scheme's capability to capture complex corrosion patterns. This highlights the power of hybrid approaches in dealing with diverse and challenging pipeline conditions. In addition, the study adds value to the existing body of work by not just assessing ML schemes for corrosion forecasting but also coupling optimization methods with the aim of improving model generalizability and deployment viability within real-world implementations<b>.</b> The findings indicate that the optimal strategy to enhance the accuracy of the recommended blended scheme's anticipations can be employed to boost the efficacy of corrosion control. This, in turn, can facilitate a digital revolution within the corrosion industry.</p>

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Innovative techniques for accurate projection of corrosion in oil and gas pipelines: support vector regression, adaptive boosting, and blended schemes

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Yibo Zhang,
  • Jierui Zhang

摘要

Pipelines in the oil and gas industry are susceptible to severe corrosion due to harsh operating conditions they encounter. Properly projecting corrosion is therefore required to render the pipelines safe and dependable. While machine learning (ML) techniques have been more commonly applied to corrosion projection, there remain certain challenges in optimizing model performance, handling data complexity, and improving interpretability for industrial purposes. Filling this gap, in the present work an attempt was made to dig deeper and develop a robust projection framework with hybrid ML schemes that are employed with improved corrosion forecast precision. Systematic discussion of diverse algorithms created to project corrosion in pipelines that are an integral part of the transmission of oil and natural gas has been illustrated. Various machine learning scenarios like SVR and AdaBoost have been employed individually and synergistically through studies. The comparison began with traditional schemes followed by hybrid schemes, using a suite of statistical metrics for comparison and validation of the schemes. The outcome was that while hybrid AdaBoost schemes had superior training capacity, SVR schemes had superior projection capabilities for pipeline corrosion, especially optimized by SMA (with R2 = 0.98687). In comparison to the traditional schemes, the hybrid schemes demonstrated considerable improvements in accuracy and reliability, efficiently reducing projection errors and enhancing the scheme's capability to capture complex corrosion patterns. This highlights the power of hybrid approaches in dealing with diverse and challenging pipeline conditions. In addition, the study adds value to the existing body of work by not just assessing ML schemes for corrosion forecasting but also coupling optimization methods with the aim of improving model generalizability and deployment viability within real-world implementations. The findings indicate that the optimal strategy to enhance the accuracy of the recommended blended scheme's anticipations can be employed to boost the efficacy of corrosion control. This, in turn, can facilitate a digital revolution within the corrosion industry.