Dong Zhenzhen, Zhang Min, Li Weirong, Wen Fenggang, Dong Guoqing, Zou Lu, Zhang Yongqiang
College of Petroleum Engineering, Xi'an Shiyou University, Xi'an 710065, China.
Shaanxi Key Laboratory of Carbon Dioxide Sequestration and Enhanced Oil Recovery, Xi'an 710075, China.
Materials (Basel). 2024 Aug 14;17(16):4046. doi: 10.3390/ma17164046.
Carbon dioxide corrosion is a pervasive issue in pipelines and the petroleum industry, posing substantial risks to equipment safety and longevity. Accurate prediction of corrosion rates and severity is essential for effective material selection and equipment maintenance. This paper begins by addressing the limitations of traditional corrosion prediction methods and explores the application of machine learning algorithms in CO2 corrosion prediction. Conventional models often fail to capture the complex interactions among multiple factors, resulting in suboptimal prediction accuracy, limited adaptability, and poor generalization. To overcome these limitations, this study systematically organized and analyzed the data, performed a correlation analysis of the data features, and examined the factors influencing corrosion. Subsequently, prediction models were developed using six algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), XGBoost, and LightGBM. The results revealed that SVM exhibited the lowest performance on both training and test sets, while RF achieved the best results with R values of 0.92 for the training set and 0.88 for the test set. In the classification of corrosion severity, RF, LightGBM, SVM, and KNN were utilized, with RF demonstrating superior performance, achieving an accuracy of 99% and an F1-score of 0.99. This study highlights that machine learning algorithms, particularly Random Forest, offer substantial potential for predicting and classifying CO2 corrosion. These algorithms provide innovative approaches and valuable insights for practical applications, enhancing predictive accuracy and operational efficiency in corrosion management.
二氧化碳腐蚀是管道和石油行业中普遍存在的问题，对设备安全和使用寿命构成重大风险。准确预测腐蚀速率和严重程度对于有效的材料选择和设备维护至关重要。本文首先阐述了传统腐蚀预测方法的局限性，并探讨了机器学习算法在二氧化碳腐蚀预测中的应用。传统模型往往无法捕捉多个因素之间的复杂相互作用，导致预测精度欠佳、适应性有限且泛化能力差。为克服这些局限性，本研究对数据进行了系统的整理和分析，对数据特征进行了相关性分析，并研究了影响腐蚀的因素。随后，使用六种算法开发了预测模型：随机森林（RF）、K近邻（KNN）、梯度提升决策树（GBDT）、支持向量机（SVM）、XGBoost和LightGBM。结果表明，SVM在训练集和测试集上的性能均最低，而RF取得了最佳结果，训练集的R值为0.92，测试集的R值为0.88。在腐蚀严重程度分类中，使用了RF、LightGBM、SVM和KNN，其中RF表现出卓越性能，准确率达到99%，F1分数为0.99。本研究强调，机器学习算法，尤其是随机森林，在预测和分类二氧化碳腐蚀方面具有巨大潜力。这些算法为实际应用提供了创新方法和宝贵见解，提高了腐蚀管理中的预测准确性和运营效率。
