中国石化国内上游数智化转型实践与展望

李冰

doi:10.13809/j.cnki.cn32-1825/te.2025556

专家论坛 | 浏览量 : 0 下载量: 190 CSCD: 0 CNKI被引量: 0 Scopus: 0 更多指标

PDF
导出
分享
收藏
专辑

中国石化国内上游数智化转型实践与展望
Practices and prospects of digital and intelligent transformation in Sinopec’s upstream sector in China
2026年16卷第2期页码：256-265
收稿：2025-12-30，

纸质出版：2026-03-26
DOI： 10.13809/j.cnki.cn32-1825/te.2025556
稿件说明：

移动端阅览

李冰. 中国石化国内上游数智化转型实践与展望[J]. 油气藏评价与开发, 2026, 16(2): 256-265. DOI： 10.13809/j.cnki.cn32-1825/te.2025556.

LI Bing. Practices and prospects of digital and intelligent transformation in Sinopec’s upstream sector in China[J]. Petroleum Reservoir Evaluation and Development, 2026, 16(2): 256-265. DOI： 10.13809/j.cnki.cn32-1825/te.2025556.

摘要

油气田企业加快数智化转型，是推进产业转型升级、培育壮大新质生产力的重要举措。中国石化国内上游深入落实建设“数智中国石化”部署，聚焦支撑石油公司改革管理，紧密结合数智技术发展趋势和勘探开发业务需求，扎实推进数智化转型。建成集团级勘探开发数据资源中心，汇聚勘探开发各类数据17.2 PB，实现了数据集中管理与共享应用；基本建成了覆盖油气生产现场的物联网，油气水井、站库数字化覆盖率分别为94.90%、92.30%，彻底改变了传统驻井驻站的人工管控模式，有力支撑了数智化条件下生产运行模式和劳动组织方式变革；统筹推进统建系统建设及深化应用，勘探开发全业务数字化覆盖程度不断提升；积极推进人工智能场景建设与试点应用，地震智能处理解释、岩石薄片智能鉴定分析、油藏智能数值模拟、智能钻井、智能压裂、工况智能诊断等场景均见到较好成效。展望“十五五”，中国石化国内上游将以建设智能油气田为目标，加快推进数据流、业务流、价值流、监督流“四流合一”，开展全业务链高价值人工智能场景建设应用，支撑“两化”融合走深走实，助力油气勘探开发生产运行效率、经营效益、管理水平的提升。

Abstract

Accelerating digital and intelligent transformation is a crucial measure for oil and gas enterprises to advance industrial transformation and upgrading and foster new productive forces. Sinopec’s upstream sector in China has thoroughly implemented the “Digital and Intelligent Sinopec” initiative

focusing on supporting corporate reform and management. By closely aligning with the development trends of digital and intelligent technologies and the demands of exploration and production operations

the digital and intelligent transformation has been steadily advanced. A group-level Exploration and Development Data Center (EPDC) has been established

aggregating 17.2 PB of various types of exploration and development data

which has enabled centralized data management and shared applications. An Internet of Things network covering oil and gas production sites has been nearly completed

with digital coverage rates for oil

gas

and water wells

and station facilities reaching 94.90% and 92.30%

respectively. This has fundamentally transformed the traditional manual management model of stationing personnel at wells and stations

effectively supporting the reform of production operation modes and labor organization under digital and intelligent conditions. The construction and deepened application of unified systems have been advanced coordinately

continuously improving the digital coverage across all exploration and development business operations. Sinopec has also actively promoted the construction of artificial intelligence (AI) scenarios and their pilot applications

achieving notable results in scenarios such as intelligent seismic processing and interpretation

intelligent rock thin-section identification and analysis

intelligent reservoir numerical simulation

intelligent drilling

intelligent fracturing

and intelligent well condition diagnosis. Looking ahead to the “15th Five-Year Plan”

Sinopec’s upstream sector in China aims to build intelligent oil and gas fields

accelerate the integration of data flow

business flow

value flow

and supervision flow (“four flows in one”)

and promote the construction and application of high-value AI scenarios across the entire business chain. These efforts will support the deeper and more substantive integration of digitalization and intellectualization

enhancing the operational efficiency

economic benefits

and management capability of oil and gas exploration

development

and production.

关键词

Keywords

references

王同良 . 油气行业数字化转型实践与思考 [J ] . 石油科技论坛 , 2020 , 39 ( 1 ): 29 - 33 .

WANG Tongliang . Practice and thinking of oil and gas industrial digitalization transformation [J ] . Petroleum Science and Technology Forum , 2020 , 39 ( 1 ): 29 - 33 .

杨剑锋 , 杜金虎 , 杨勇 , 等 . 油气行业数字化转型研究与实践 [J ] . 石油学报 , 2021 , 42 ( 2 ): 248 - 258 .

YANG Jianfeng , DU Jinhu , YANG Yong , et al . Research and practice on digital transformation of the oil and gas industry [J ] . Acta Petrolei Sinica , 2021 , 42 ( 2 ): 248 - 258 .

李阳 , 杨勇 . 老油田绿色低成本开发探索与实践 [J ] . 油气地质与采收率 , 2019 , 26 ( 2 ): 1 - 6 .

LI Yang , YANG Yong . Exploration and practice of green low-cost development in old oilfields [J ] . Petroleum Geology and Recovery Efficiency , 2019 , 26 ( 2 ): 1 - 6 .

李冰 , 郭洪金 , 段鸿杰 . 中国石化油气生产数智化建设的探索与实践 [J ] . 油气地质与采收率 , 2025 , 32 ( 2 ): 81 - 93 .

LI Bing , GUO Hongjin , DUAN Hongjie . Exploration and practice of digital and intelligent construction in Sinopec’s oil and gas production [J ] . Petroleum Geology and Recovery Efficiency , 2025 , 32 ( 2 ): 81 - 93 .

吴海莉 , 龚仁彬 . 中国石油油气生产数字化智能化发展思考 [J ] . 石油科技论坛 , 2023 , 42 ( 6 ): 9 - 17 .

WU Haili , GONG Renbin . Thinking on digital and intelligent development of CNPC’s oil and gas production [J ] . Petroleum Science and Technology Forum , 2023 , 42 ( 6 ): 9 - 17 .

王博弘 , 彭龙 , 解晓智 , 等 . 深海气田开发人工智能技术应用现状及展望 [J ] . 天然气工业 , 2025 , 45 ( 8 ): 88 - 102 .

WANG Bohong , PENG Long , XIE Xiaozhi , et al . Application of artificial intelligence in deep-sea gas field development technologies: Review and prospect [J ] . Natural Gas Industry , 2025 , 45 ( 8 ): 88 - 102 .

刘文岭 , 韩大匡 . 数字孪生油气藏: 智慧油气田建设的新方向 [J ] . 石油学报 , 2022 , 43 ( 10 ): 1450 - 1461 .

LIU Wenling , HAN Dakuang . Digital twin system of oil and gas reservoirs: A new direction for smart oil and gas field construction [J ] . Acta Petrolei Sinica , 2022 , 43 ( 10 ): 1450 - 1461 .

匡立春 , 刘合 , 任义丽 , 等 . 人工智能在石油勘探开发领域的应用现状与发展趋势 [J ] . 石油勘探与开发 , 2021 , 48 ( 1 ): 1 - 11 .

KUANG Lichun , LIU He , REN Yili , et al . Application and development trend of artificial intelligence in petroleum exploration and development [J ] . Petroleum Exploration and Development , 2021 , 48 ( 1 ): 1 - 11 .

张凯 , 赵兴刚 , 张黎明 , 等 . 智能油田开发中的大数据及智能优化理论和方法研究现状及展望 [J ] . 中国石油大学学报(自然科学版) , 2020 , 44 ( 4 ): 28 - 38 .

ZHANG Kai , ZHAO Xinggang , ZHANG Liming , et al . Current status and prospect for the research and application of big data and intelligent optimization methods in oilfield development [J ] . Journal of China University of Petroleum (Edition of Natural Science) , 2020 , 44 ( 4 ): 28 - 38 .

CORNELIO J , MOHD RAZAK S , CHO Y , et al . Residual learning to integrate neural network and physics-based models for improved production prediction in unconventional reservoirs [J ] . SPE Journal , 2022 , 27 ( 6 ): 3328 - 3350 .

LI D L , WANG Z Q , ZHA W S , et al . Predicting production-rate using wellhead pressure for shale gas well based on Temporal Convolutional Network [J ] . Journal of Petroleum Science and Engineering , 2022 , 216 : 110644 .

LI X C , MA X F , XIAO F C , et al . Multistep ahead multiphase production prediction of fractured wells using bidirectional gated recurrent unit and multitask learning [J ] . SPE Journal , 2023 , 28 ( 1 ): 381 - 400 .

吴淑红 , 毕剑飞 , 范天一 , 等 . 油藏数值模拟智能化技术思考与展望 [J ] . 石油科技论坛 , 2024 , 43 ( 6 ): 96 - 106 .

WU Shuhong , BI Jianfei , FAN Tianyi , et al . Thoughts and prospects on the intelligent transformation of reservoir simulation [J ] . Petroleum Science and Technology Forum , 2024 , 43 ( 6 ): 96 - 106 .

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 .

PAN Huanquan , LIU Jianqiao , GONG Bin , et al . Construction and preliminary application of large language model for reservoir performance analysis [J ] . Petroleum Exploration and Development , 2024 , 51 ( 5 ): 1175 - 1182 .

陈志明 , 朱海锋 , 赵辉 , 等 . 基于特征点和改进卷积神经网络的压裂水平井裂缝参数高效快速反演 [J ] . 石油学报 , 2025 , 46 ( 9 ): 1751 - 1763 .

CHEN Zhiming , ZHU Haifeng , ZHAO Hui , et al . Efficient and rapid inversion of fracture parameters of fracturing horizontal wells based on feature points and improved CNN [J ] . Acta Petrolei Sinica , 2025 , 46 ( 9 ): 1751 - 1763 .

石玉江 , 周军 , 李雄伟 , 等 . 测井人工智能应用场景及实现 [J ] . 石油科技论坛 , 2024 , 43 ( 6 ): 28 - 37 .

SHI Yujiang , ZHOU Jun , LI Xiongwei , et al . Scenario and realization of logging artificial intelligence application [J ] . Petroleum Science and Technology Forum , 2024 , 43 ( 6 ): 28 - 37 .

胡慧芳 , 张世明 , 曹小朋 , 等 . 基于图神经网络的井间注采动态响应研究 [J ] . 油气地质与采收率 , 2023 , 30 ( 4 ): 130 - 136 .

HU Huifang , ZHANG Shiming , CAO Xiaopeng , et al . Research on dynamic response of interwell injection-production based on graph neural network [J ] . Petroleum Geology and Recovery Efficiency , 2023 , 30 ( 4 ): 130 - 136 .

刘子雄 , 钦伟 , 贺卫东 , 等 . 致密气井人工智能压裂参数优化 [J ] . 中国海上油气 , 2025 , 37 ( 4 ): 104 - 112 .

LIU Zixiong , QIN Wei , HE Weidong , et al . Artificial intelligence optimization of fracturing parameters for tight gas wells [J ] . China Offshore Oil and Gas , 2025 , 37 ( 4 ): 104 - 112 .

马涛 , 王铁成 , 卫乾 . “十四五”油气上游业务数智化转型实践与展望 [J ] . 石油科技论坛 , 2025 , 44 ( 3 ): 20 - 30 .

MA Tao , WANG Tiecheng , WEI Qian . Practice and prospect of smart upstream oil and gas transition in 14th Five-Year Plan period [J ] . Petroleum Science and Technology Forum , 2025 , 44 ( 3 ): 20 - 30 .

单彤文 . 中国海油人工智能发展探索与实践 [J ] . 中国海上油气 , 2024 , 36 ( 6 ): 177 - 185 .

SHAN Tongwen . CNOOC’s exploration and practice in development of artificial intelligence [J ] . China Offshore Oil and Gas , 2024 , 36 ( 6 ): 177 - 185 .

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 .

李四海 , 乔雨露 , 夏钦锋 . 基于云原生的智能气田建设研究与实践 [J ] . 石油科技论坛 , 2024 , 43 ( 6 ): 56 - 62 .

LI Sihai , QIAO Yulu , XIA Qinfeng . Research and practice of smart gas field construction based on cloud native [J ] . Petroleum Science and Technology Forum , 2024 , 43 ( 6 ): 56 - 62 .

聂晓炜 . 智能油田关键技术研究现状与发展趋势 [J ] . 油气地质与采收率 , 2022 , 29 ( 3 ): 68 - 79 .

NIE Xiaowei . Research status and development trend of core technologies of intelligent oilfields [J ] . Petroleum Geology and Recovery Efficiency , 2022 , 29 ( 3 ): 68 - 79 .

段鸿杰 , 马承杰 , 董琰 , 等 . 胜利油田数字化转型实践与成效 [J ] . 石油科技论坛 , 2023 , 42 ( 3 ): 48 - 55 .

DUAN Hongjie , MA Chengjie , DONG Yan , et al . Practice and achievements from digital transformation in Shengli Oilfield [J ] . Petroleum Science and Technology Forum , 2023 , 42 ( 3 ): 48 - 55 .

段鸿杰 , 马承杰 , 王振 , 等 . 胜利油田油气认知大模型建设与应用 [J ] . 石油科技论坛 , 2024 , 43 ( 6 ): 46 - 55 .

DUAN Hongjie , MA Chengjie , WANG Zhen , et al . Large Language Model of Oil and Gas Cognition Constructed and Applied in Shengli Oilfield [J ] . Petroleum Science and Technology Forum , 2024 , 43 ( 6 ): 46 - 55 .

糜利栋 , 曾大乾 , 李遵照 , 等 . 中国石化地下储气库智能化建设进展及展望 [J ] . 世界石油工业 , 2023 , 30 ( 6 ): 88 - 95 .

MI Lidong , ZENG Daqian , LI Zunzhao , et al . Progress and prospect of intelligent of Sinopec underground gas storage [J ] . World Petroleum Industry , 2023 , 30 ( 6 ): 88 - 95 .

糜利栋 , 曾大乾 , 刘华 , 等 . 天然气地下储气库智能化建设关键技术及其发展趋势 [J ] . 石油与天然气地质 , 2024 , 45 ( 2 ): 581 - 592 .

MI Lidong , ZENG Daqian , LIU Hua , et al . Key technologies and development trends for intelligent construction of underground gas storage facilities [J ] . Oil & Gas Geology , 2024 , 45 ( 2 ): 581 - 592 .

韩昊霖 , 王冕 . 国内外数字化转型成熟度模型对比分析 [J ] . 信息技术与标准化 , 2025 ( 1 ): 7 - 12 .

HAN Haolin , WANG Mian . Comparative analysis of domestic and international digital transformation maturity models [J ] . Information Technology & Standardization , 2025 ( 1 ): 7 - 12 .

MELBERG K , GRESSGÅRD L J . Digitalization and changes to work organization and management in the Norwegian petroleum industry [J ] . Cognition , Technology & Work, 2023 , 25 ( 4 ): 447 - 460 .

曾涛 , 袁园 . 国际油服公司数字化转型和智能化发展策略分析 [J ] . 国际石油经济 , 2022 , 30 ( 7 ): 36 - 43 .

ZENG Tao , YUAN Yuan . Analysis on digital transition and intelligent development of international oilfield service companies [J ] . International Petroleum Economics , 2022 , 30 ( 7 ): 36 - 43 .

曾涛 , 刘晗光 , 高坚 . 斯伦贝谢公司数字化转型的经验与启示 [J ] . 国际石油经济 , 2021 , 29 ( 1 ): 94 - 99 .

ZENG Tao , LIU Hanguang , GAO Jian . Experience and inspiration from Schlumberger’s digital transition [J ] . International Petroleum Economics , 2021 , 29 ( 1 ): 94 - 99 .

刘合 , 任义丽 , 李欣 , 等 . 油气行业人工智能大模型应用研究现状及展望 [J ] . 石油勘探与开发 , 2024 , 51 ( 4 ): 910 - 923 .

LIU He , REN Yili , LI Xin , et al . Research status and application of artificial intelligence large models in the oil and gas industry [J ] . Petroleum Exploration and Development , 2024 , 51 ( 4 ): 910 - 923 .

李欣 , 王洪亮 , 闫林 , 等 . 油气人工智能技术发展综述与体系框架构想 [J ] . 国际石油经济 , 2025 , 33 ( 5 ): 10 - 18 .

LI Xin , WANG Hongliang , YAN Lin , et al . Review of AI technology development in oil and gas fields and framework architecture conceptualization [J ] . International Petroleum Economics , 2025 , 33 ( 5 ): 10 - 18 .

杨明澔 , 李小波 , 刘兴邦 , 等 . 人工智能大模型在油气勘探开发领域的应用及挑战 [J ] . 石油科技论坛 , 2024 , 43 ( 6 ): 107 - 113 .

YANG Minghao , LI Xiaobo , LIU Xingbang , et al . Application of artificial intelligence pretrained foundation models in exploration and development and challenges in this area [J ] . Petroleum Science and Technology Forum , 2024 , 43 ( 6 ): 107 - 113 .

于强 , 王宝江 , 张禄明 , 等 . 深度学习技术在油气勘探中的研究进展与应用挑战 [J ] . 天然气工业 , 2025 , 45 ( 5 ): 43 - 56 .

YU Qiang , WANG Baojiang , ZHANG Luming , et al . Research progress and application challenges of deep learning technology in oil and gas exploration [J ] . Natural Gas Industry , 2025 , 45 ( 5 ): 43 - 56 .

文章被引用时，请邮件提醒。

提交

暂无数据