AI赋能下石油工程专业本科生产实习教学模式创新与实践：以采油工程为例

Innovation and Practice in Undergraduate Production Internship Teaching Models for Petroleum Engineering Majors Empowered by AI: A Case Study of Oil Production Engineering

摘要: 在AI技术飞速发展的背景下，油田行业面临新的挑战与机遇，传统的采油工程生产实习面临传统教学模式与智能化人才需求的显著矛盾，传统的采油工程专业生产实习模式亟需改革。本文旨在探讨AI赋能背景下，如何有效提升采油工程专业学生的实践能力和职业素养。通过分析当前油田AI技术的应用现状，针对实习内容滞后、校企协同低效、AI赋能不足等核心问题，结合AI赋能工具(如物联网、数据分析、虚拟现实等)在实习中的应用进行探索，总结了AI赋能下的采油工程生产实习改革方案，提出了一系列教育改革和实施建议，为高等院校课程设置和实习安排提供参考，以期培养符合行业需求的高素质工程人才。

Abstract: With the rapid development of AI technology, the oilfield industry faces new challenges and opportunities. Traditional oil production engineering production internships face a significant contradiction between traditional teaching models and the demand for intelligent talent. The traditional production internship model for oil production engineering majors urgently needs reform. This paper aims to explore how to effectively improve the practical skills and professional qualities of oil production engineering students in the context of AI empowerment. By analyzing the current application status of AI technology in the oilfield, addressing core issues such as lagging internship content, inefficient school-enterprise collaboration, and insufficient AI empowerment, and exploring the application of AI-enabled tools (such as the Internet of Things, data analysis, and virtual reality) in internships, this paper summarizes reform plans for oil production engineering production internships empowered by AI and proposes a series of educational reform and implementation suggestions. These suggestions provide a reference for curriculum design and internship arrangements in higher education institutions, aiming to cultivate high-quality engineering talents that meet industry needs.