油气行业人工智能学科建设研究与思考

doi:10.3969/j.issn.1002-302X.2024.06.001

摘要/Abstract

摘要： 为了加快我国油气人工智能一级学科体系建设和人才培养模式转变，解决油气行业人工智能人才不足与社会及企业需求不协调的矛盾，文章依据教育部2018年印发的《高等院校人工智能创新行动计划》，分析了全球人工智能研究与人才的竞争态势，指出只有建设好人工智能学科体系，才能推动我国高校和研究院所全面提升人工智能人才培养质量，助力教学与科研不断发展。深入研究了国内外15所高校和研究机构的人工智能学科建设现状，指出国内外综合性大学、工科院校、研究院学校都未将人工智能作为一级学科建设，明显滞后于企业业务需求和社会需求，影响了人工智能的人才教育和人才培养。剖析了油气行业人工智能学科建设在理论教学、实践训练、项目实践等方面存在的挑战，提出了油气行业人工智能人才培养模式和学科建设的方向与系统框架，构建了“AI+油气业务”和“油气业务+AI”相结合的复合型人才培养体系，更新人才培养模式，推动人工智能学科创新发展及人工智能技术在油气行业的广泛落地应用。

关键词: 石油天然气；人工智能；学科建设；机器学习

Abstract: In order to accelerate the construction of the first-level discipline system of artificial intelligence in the oil and gas industry and the transformation of the talent training model in China, and to solve the contradiction between the shortage of artificial intelligence talents in the oil and gas industry and the uncoordinated demands of society and enterprises, based on the“ Action Plan for Artificial Intelligence Innovation at Universities and Colleges” issued by the Ministry of Education in 2018, the Ministry of Education of China, this article analyzes the global competitive situation of artificial intelligence research and talents, and points out that only by building a good artificial intelligence discipline system can we promote comprehensive improvement of the quality of artificial intelligence talent training in Chinese universities and research institutes, and facilitate the continuous development of teaching and research. It deeply studies the current situation of artificial intelligence discipline construction in 15 domestic and foreign universities and research institutions, and points out that domestic and foreign comprehensive universities, engineering colleges, and research institute schools have not constructed artificial intelligence as a first-level discipline, which lags significantly behind the business and social demands of enterprises, affecting the talent education and training of artificial intelligence. It analyzes the challenges existing in the discipline construction of artificial intelligence in the oil and gas industry in terms of theoretical teaching, practical training, and project practice, proposes the direction and system framework of the talent training model and discipline construction of artificial intelligence in the oil and gas industry, builds a compound talent training system combining “AI+oil and gas business” and “oil and gas business+AI”, updates the talent training model, and promotes the innovative development of the artificial intelligence discipline and the wide application of artificial intelligence technology in the oil and gas industry.

Key words: oil and natural gas; artificial intelligence; construction of disciplines; machine-learning

中图分类号:

刘合，李欣,窦宏恩，闫林，王洪亮，刘俊榜，李小波，任义丽，李宁. 油气行业人工智能学科建设研究与思考[J]. 石油科技论坛, 2024, 43(6): 1-12.

Liu He, Li Xin, Dou Hong’en, Yan Lin, Wang Hongliang,Liu Junbang, Li Xiaobo, Ren Yili, Li Ning. Research and Thinking on Discipline Construction of Artificial Intelligence in Oil and Gas Industry[J]. Petroleum Science and Technology Forum, 2024, 43(6): 1-12.

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