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

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中国石化国内上游数智化转型实践与展望

专家论坛 | 更新时间：2026-03-10

- 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
- 油气藏评价与开发 2026年16卷第2期页码：256-265
- 作者机构：
  
  中国石化油田勘探开发事业部，北京 100728
- 作者简介：
  
  李冰（1981—），男，硕士，高级经济师，主要从事企业改革与管理、油气勘探开发数智化、地面工程、设备设施等管理工作。地址：北京市朝阳区朝阳门北大街22号，邮政编码：100728。E-mail：libing@sinopec.com
- 基金信息：
- DOI：10.13809/j.cnki.cn32-1825/te.2025556
  中图分类号： TE319
- 收稿：2025-12-30，
  
  纸质出版：2026-03-26
- 稿件说明：
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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.

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.
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

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