Exploring the path of empowering the natural gas industry with “AI+”

doi:10.12267/j.issn.2096-5931.2025.08.005

Abstract

Abstract:

Currently, artificial intelligence (AI), as a representative of emerging industries, has become a major driving force in the global technological wave. From its early stages of simple logical reasoning to the current era of autonomous deep learning, complex task processing, and intelligent decision-making support, AI has shaped a new paradigm for the development of the modern era, profoundly impacting various fields such as society, economy, and culture. As a key sector for national energy reserves and public welfare, the energy industry is accelerating its transformation from “manual” to “intelligent” driven by AI. Emerging technologies such as automated production, remote monitoring, data analysis, production simulation, and intelligent transportation are gradually becoming the core drivers for improving productivity and upgrading the industrial structure.

Key words: AI, large models, natural gas industry

CLC Number:

F426.22
TP18

HU Wenjuan, YAN Zhaokun, MENG Danni, XU Shan, JIANG Bocheng, XU Lijiao, CHEN Guangtuo, ZHANG Yi. Exploring the path of empowering the natural gas industry with “AI+”[J]. Information and Communications Technology and Policy, 2025, 51(8): 35-41.

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URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2025.08.005

http://ictp.caict.ac.cn/EN/Y2025/V51/I8/35

Figures/Tables 3

References 20

[1]	吴恩华, 柳有权. 基于图形处理器(GPU)的通用计算[J]. 计算机辅助设计与图形学学报, 2004, 16(5):601-612.
[2]	SHERSTINSKY A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network[J]. Physica D: Nonlinear Phenomena, 2020, 404: 132306.
[3]	HAN K, XIAO A, WU E, et al. Transformer in transformer[J]. Advances in Neural Information Processing Systems, 2021,34: 15908-15919.
[4]	LIU X, ZHENG Y, DU Z, et al. GPT understands, too[J]. AI Open, 2024, 5: 208-215.
[5]	郭华. 深度学习及其意义[J]. 课程·教材·教法, 2016, 36(11):25-32.
[6]	高阳, 陈世福, 陆鑫. 强化学习研究综述[J]. 自动化学报, 2004(1):86-100.
[7]	YURTSEVER E, LAMBERT J, CARBALLO A, et al. A survey of autonomous driving: common practices and emerging technologies[J]. IEEE Access, 2020, 8: 58443-58469.
[8]	杨挺, 赵黎媛, 王成山. 人工智能在电力系统及综合能源系统中的应用综述[J]. 电力系统自动化, 2019, 43(1):2-14.
[9]	刘勇. 液化天然气的危险性与安全防护[J]. 天然气工业, 2004(7):105-107,142.
[10]	武洪昆, 季元旗, 王晓庆, 等. LNG国际采购与我国天然气市场匹配问题研究[J]. 国际石油经济, 2019, 27(12):37-41.
[12]	林弘光, 蒲艾婧, 王建敏. 基于供求关系的天然气市场营销策略及其实证研究[J]. 天然气技术与经济, 2016, 10(1):66-69,84. doi: 10.3969/j.issn.2095-1132.2016.01.020
[13]	张瑞君, 于涛. 中国企业集团司库体系构建的战略思考:以中国石油天然气集团公司的实践为例[J]. 财务与会计, 2010(7):37-39.
[14]	Energy Institute. Statistical review of world energy 2025[R], 2025.
[15]	陈栋. 从信息孤岛到协同管理:项目经济数字化转型的实施路径研究[J]. 中国信息化, 2025(4):51-54.
[16]	陈兵, 董思琰. 人工智能通用大模型市场竞争治理架构探设[J]. 上海财经大学学报, 2025, 27(3):124-137.
[17]	宫敬, 吴冕, 赵周丙, 等. 油气管网行业大模型的思考及应用探索[J]. 油气储运, 2025, 44(4):379-393.
[18]	潘焕泉, 刘剑桥, 龚斌, 等. 油藏动态分析场景大模型构建与初步应用[J]. 石油勘探与开发, 2024, 51(5):1175-1182. doi: 10.11698/PED.20240208
[19]	Saipem. Saipem’s subsea drone for inspection of Shell and Petrobras fields in Brazil[EB/OL]. (2022-05-24)[2025-06-10]. https://www.saipem.com/en/media/press-releases/2022-05-24/saipem-subsea-drone-inspection-shell-and-petrobras-fields-brazil.
[20]	胡汉舟. 绿色转型加快推进能源保障全面增强[EB/OL]. (2025-01-17)[2025-06-10]. http://www.ce.cn/xwzx/gnsz/gdxw/202501/17/t20250117_39269936.shtml.

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