油气领域人工智能标准化工作思考与建议

doi:10.3969/j.issn.1002-302X.2024.05.006

石油科技论坛 ›› 2024, Vol. 43 ›› Issue (5): 35-42.DOI: 10.3969/j.issn.1002-302X.2024.05.006

油气领域人工智能标准化工作思考与建议

闫林^1,2,3,李欣^1,2,3,李小波^1,2,3,刘俊榜^1,2,3,王洪亮^1,2,李心浩^1,2,贾文清¹

1. 中国石油勘探开发研究院； 2. 中国石油天然气集团有限公司勘探开发人工智能技术研发中心；3. 多资源协同陆相页岩油绿色开采全国重点实验室

出版日期:2024-10-31 发布日期:2024-11-28
作者简介:闫林，1978年生，2007年毕业于中国石油勘探开发研究院，博士，高级工程师，现主要从事油气人工智能应用技术研究工作。
基金资助:
国家重点研发计划“地质资源精准开发风险预测的大数据智能分析技术及平台建设”（编号：2022YFF0801200）；中国石油天然气集团有限公司前瞻性基础性技术攻关项目“油气勘探开发人工智能关键技术研究”（编号：2023DJ84）。

Thinking and Suggestions on Artificial Intelligence Standardization in Oil and Gas Field

Yan Lin^1,2,3,Li Xin^1,2,3,Li Xiaobo^1,2,3,Liu Junbang^1,2,3,Wang Hongliang^1,2,Li Xinhao^1,2,Jia Wenqing¹

1. PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China; 2. Artificial Intelligence Technology R & D Center for Exploration and Development, CNPC, Beijing 100083, China; 3. State Key Laboratory of Continental Shale Oil, Daqing 163712, China

Online:2024-10-31 Published:2024-11-28

摘要/Abstract

摘要： 近年来，人工智能技术飞速发展，在垂直行业的规模应用实效显著，国内外均意识到人工智能标准工作的重要性，积极推进人工智能标准工作。人工智能技术在油气领域的融合应用，整体按“单点智能技术—智能化技术系列—大模型”路径演进、扩展，当前已实现多点技术突破，推进油气人工智能标准化工作的重要性和必要性凸显。建议采用“整体布局、分层细化、逐步完善”的方式，分基础骨干、重点方向、应用场景3个层次构建油气人工智能标准体系，明晰油气人工智能与其他专业界限、科学组建油气人工智能标准委员会，协同推进油气人工智能标准化工作快速有序发展。

关键词: 人工智能；油气行业；标准化工作；体系

Abstract: Artificial intelligence (AI) technology has been under rapid development in recent years with the significant results achieved in its large-scale application in the vertical industries. AI standardization was stressed for its importance and actively accelerated both at home and abroad. AI technology was used in the oil and gas industry. Its integration and application was demonstrated and expanded along the “single-point intelligence technology-intelligence technology series-large model” roadway.Currently, a breakthrough has been made in multi-point technology, highlighting the importance and necessity of accelerating oil and gas AI standardization. It is proposed to adopt the method of “deployment as a whole, differentiation in various levels and gradual perfection.” Construction of the oil and gas AI standards system is based on three levels--basic backbone, key orientation and application scenarios--to define the boundaries between the oil and gas AI and other disciplines and scientifically organize the oil and gas AI standards committee, thus bringing about a rapid and healthy development of oil and gas AI standardization.

Key words: artificial intelligence; oil and gas industry; standardization; system

中图分类号:

闫林,李欣,李小波,刘俊榜,王洪亮,李心浩,贾文清. 油气领域人工智能标准化工作思考与建议[J]. 石油科技论坛, 2024, 43(5): 35-42.

Yan Lin, Li Xin, Li Xiaobo, Liu Junbang, Wang Hongliang, Li Xinhao, Jia Wenqing. Thinking and Suggestions on Artificial Intelligence Standardization in Oil and Gas Field[J]. Petroleum Science and Technology Forum, 2024, 43(5): 35-42.

参考文献

[1] 李剑峰. 油气工业数字化智能化发展趋势[J]. 石油科技论坛, 2023,42(3): 10-21.
Li Jianfeng. Digital and intelligent development trend of oil and gas industry[J]. Petroleum Science and Technology Forum, 2023, 42(3):10-21.
[2] 谢晓辉, 安鹏. 中国海油人工智能建设探索与实践[J]. 石油科技论坛, 2023, 42(3): 22-29.
Xie Xiaohui, An Peng. Research and practice of CNOOC AI application[J]. Petroleum Science and Technology Forum, 2023, 42(3):22-29.
[3] 张熙, 杨小汕, 徐常胜. ChatGPT 及生成式人工智能现状及未来发展方向[J]. 中国科学基金, 2023, 37(5): 743-750.
Zhang Xi, Yang Xiaoshan, Xu Changsheng. Current state and future development directions of ChatGPT and generative artificial intelligence[J]. Bulletin of National Natural Science Foundation of China, 2023, 37(5): 743-750.
[4] 李杨, 杜雷雷, 许飞, 等. 大数据与人工智能在医学领域的应用进展[J]. 协和医学杂志, 2023, 14 (1): 184-189.
Li Yang, Du Leilei, Xu Fei, et al. Big data and artificial intelligence in medicine[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14 (1): 184-189.
[5] 贾积有, 张誉月. 人工智能与教育: 机遇、挑战与对策[J]. 北京大学教育评论, 2023, 21(1): 55-67.
Jia Jiyou, Zhang Tengyue. Artificial intelligence and education: Opportunity, challenge and strategy[J]. Peking University Education Review, 2023, 21(1): 55-67.
[6] 廖高可, 李庭辉. 人工智能在金融领域的应用研究进展[J]. 经济学动态, 2023(3): 143-160.
Liao Gaoke, Li Tinghui. Research progress on the applications of artificial intelligence in finance[J]. Economic Perspectives, 2023(3):143-160.
[7] 李剑峰, 肖波, 肖莉, 等. 智能油田[M]. 北京: 中国石化出版社, 2020.
Li Jianfeng, Xiao Bo, Xiao Li, et al. Intelligent oilfield[M]. Beijing: China Petrochemical Press, 2020.
[8] 布莱恩·考科思, 杰夫·福修. 量子宇宙: 一切可能发生的正在发生[M]. 伍义生, 余瑾, 译. 重庆: 重庆出版社, 2013.
Cox B, Forshaw J. The quantum universe: Everything that can happen does happen[M]. Wu Yisheng, Yu Jin, translated. Chongqing:Chongqing Press, 2013.
[9] 蔡亚森. 中国人工智能标准发展趋势[C]//. 中国标准化协会. 中国标准化年度优秀论文(2022)论文集, 2022: 464-465.
Cai Yasen. China’s artificial intelligence standardization development trend[C]// China Association for Standardization. Collection of annual excellent papers (2022) of China standardization, 2022: 464-465.
[10] 肖倚天, 孙旭东. 综合性AI 技术引领油气勘探智能化发展未来[J].中国石化, 2024(1): 46-50.
Xiao Yitian, Sun Xudong. Comprehensive artificial intelligence technology guides intelligent development future of oil and gas exploration[J]. Sinopec Monthly, 2024(1): 46-50.
[11] 刘合. 为数智中国石油建设赋能[J]. 中国石油石化, 2023(7): 16-19.
Liu He. Empower building of intelligent and digital CNPC[J]. China Petrochem, 2023(7): 16-19.
[12] 郑中. 铁路工程人工智能技术标准体系研究[J]. 国防交通工程与技术, 2024, 22(3): 3-4.
Zheng Zhong. Research on standard system of artificial intelligence technology in railway engineering[J]. Traffic Engineering and Technology for National Defence, 2024, 22(3): 3-4.

油气领域人工智能标准化工作思考与建议

Thinking and Suggestions on Artificial Intelligence Standardization in Oil and Gas Field

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