腐蚀大数据分析技术融入《油气田腐蚀与防护》课程的教改探索

Integrating Corrosion Big Data Analysis Technology into the “Oil and Gas Field Corrosion and Protection” Course: An Exploration of Teaching Reform

摘要: 为了满足油气田生产、存储和运输领域在金属设施的腐蚀防护方面对既懂专业又懂腐蚀防护的智能化技术的需求，本文旨在改变以往《油气田腐蚀与防护》课程单一的以油气田腐蚀机理和防护经验为主的教学体系，探讨将油气田腐蚀大数据分析技术系统性融入该课程教学的必要性、具体融合内容、创新教学方法及预期成效。通过构建“机理–数据”双轮驱动的教学新范式，培养学生利用多源数据进行分析、预测和决策的能力，从而提升课程的前沿性与实用性，为油气行业输送具备面向未来和数据科学素养的新型复合型人才。

Abstract: To address professional demand for intelligent corrosion protection technologies that combine professional expertise with corrosion prevention in the production, storage, and transportation of oil and gas fields, this paper aims to reform the traditional teaching system of the “Oil and Gas Field Corrosion and Protection” course, which has predominantly focused on corrosion mechanisms and empirical protection methods, discussing the necessity of systematically integrating big data analysis technology into oil and gas field corrosion and protection curriculum, specific integration content and innovative teaching methods, and expected outcomes. By establishing a new “mechanism-data” dual-driven teaching paradigm, this study will cultivate students’ ability to analyze, predict, and make decisions using multi-source data, which will enhance the course’s relevance to advanced and practical applications, thereby preparing future-oriented, data-survey multidisciplinary talent for oil and gas industry.