超临界CO₂注入井筒腐蚀预测模型研究进展

Research Progress on Corrosion Prediction Models of Supercritical CO₂ Injection Wellbores

摘要: 超临界CO₂注入井筒作为CCUS技术工程化应用的核心环节，面临高温高压、多杂质耦合、多相流冲刷的复杂腐蚀环境，井筒腐蚀失效直接威胁工程安全与长效运行，而精准的腐蚀预测是开展腐蚀防控的关键前提。本文以超临界CO₂注入井筒腐蚀预测模型为研究核心，系统梳理了传统经验模型、半经验模型、机理模型的研究现状、适用范围及局限性，重点介绍了机器学习、神经网络等前沿算法在腐蚀预测中的应用进展，分析了不同模型在超临界CO₂井筒腐蚀预测中的适配性与现存问题，最后展望了腐蚀预测模型的未来发展方向。研究表明：传统模型虽奠定了腐蚀预测的理论基础，但受限于多因素耦合作用的复杂性，难以适配超临界CO₂井筒的极端工况；以机器学习、神经网络为代表的智能预测模型，凭借强非线性拟合与多源数据处理能力，成为超临界CO₂腐蚀预测的前沿发展方向，但目前仍存在样本数据不足、模型可解释性差、工程化验证缺乏等问题。未来需推动传统机理模型与智能算法的融合，构建多场耦合、数据驱动的一体化腐蚀预测模型，为超临界CO₂注入井筒的腐蚀防控提供精准技术支撑。

Abstract: As the core link in the engineering application of CCUS technology, supercritical CO₂ injection wells face a complex corrosion environment characterized by high temperature, high pressure, multi-impurity coupling, and multiphase flow erosion. Wellbore corrosion failure directly threatens engineering safety and long-term operation, and accurate corrosion prediction is a critical prerequisite for corrosion prevention and control. This paper focuses on corrosion prediction models for supercritical CO₂ injection wells. It systematically reviews the research status, applicable scope, and limitations of traditional empirical models, semi-empirical models, and mechanistic models. It highlights the application progress of advanced algorithms such as machine learning and neural networks in corrosion prediction, and analyzes the adaptability and existing problems of different models in supercritical CO₂ wellbore corrosion prediction. Finally, the future development trends of corrosion prediction models are prospected. Studies show that although traditional models have laid a theoretical foundation for corrosion prediction, they are limited by the complexity of multi-factor coupling and are difficult to adapt to the extreme working conditions of supercritical CO₂ wells. Intelligent prediction models represented by machine learning and neural networks have become the frontier development direction of supercritical CO₂ corrosion prediction by virtue of their strong nonlinear fitting and multi-source data processing capabilities. However, they still suffer from insufficient sample data, poor model interpretability, and lack of engineering verification. In the future, it is necessary to promote the integration of traditional mechanistic models and intelligent algorithms, and build an integrated corrosion prediction model driven by multi-field coupling and data, so as to provide accurate technical support for the corrosion prevention and control of supercritical CO₂ injection wells.