Wang Han. Application status and thinking of artificial intelligence in refining and chemical field[J]. Petroleum Refinery Engineering, 2025, 55(2): 12-15.
[1] 龚仁彬,杨燕子,任义丽,等. 知识图谱在石油勘探开发领域的应用现状及发展趋势 [J]. 信息系统工程,2021,34(9):16-18. [2] 刘万伟,刘瑞超,张鸣歌. 石油勘探开发知识管理技术研究与应用 [J]. 大庆石油地质与开发,2019,38(5):290-293. [3] 鲁强,刘兴昱. 基于迁移学习的知识图谱问答语义匹配模型 [J]. 计算机应用,2018,38(7):1846-1852. [4] WANG Q F, LIU J H, LIU J J, et al. Intrinsic safety & reliability and supervision intelligentization of equipment in refinery enterprises [J]. Strategic study of Chinese Academy of Engineering, 2019, 21(6): 129-136. [5] 陆桃妹,唐弟官. 大数据背景下基于LNP**S专利分析的国内外页岩压裂技术进展 [J]. 石油科学通报,2019(2):154-164. [6] XAVIER W, ANDRE M T, et al. Remote sensing technologies for detecting, visualizing and quantifying gas leaks [C]// SPE International Conference and Exhibition on Health, Safety, Security Environment, and Social Responsibility 2018. Abu Dhabi: United Arab Emirates, 2018. [7] BRASWELL G. Artificial intelligence comes of age in oil and gas [J]. Journal of petroleum technology, 2013(1): 50-56. [8] KHAN M R, ALNUAIM S, TARIQ Z, et al. Machine learning application for oil rate prediction in artificial gas lift wells [C]. SPE Middle East Oil and Gas Show and Conference Manama, Bahrain, 2019. [9] HAN W, LUIS A R. Dynamic optimization of a pilot-scale entrained-flow gasifier using artificial recurrent neural networks [J]. Fuel, 2020(272): 117731. [10] HOJAGELDIYEV D. Artificial intelligence in HSE [C]// SPE Abu Dhabi International Petroleum Exhibition & Conference. Abu Dhabi: United Arab Emirates, 2018. [11] 孙雪婷,傅钰江,林堂茂,等. 基于计算机视觉的石化火灾智能监测研究 [J]. 炼油技术与工程,2024,54(8):51-55. [12] 王国彤,孙秉才,储胜利,等. 炼化企业智能机器人巡检技术应用前景分析 [J]. 炼油技术与工程,2019,49(9):35-38.