石油科技论坛 ›› 2019, Vol. 38 ›› Issue (6): 34-42.DOI: 10.3969/j.issn.1002-302x.2019.06.007

• 技术创新 • 上一篇    下一篇

油气上游领域智能化发展方向探析

贾鹿1 牛志杰2 石国伟3 李嗣旭1 林道寿4   

  1. 1. 中国石油新疆油田公司数据公司信息研究所;2. 中国石油新疆油田公司勘探开发研究院勘探所; 3. 中国石油新疆油田公司数据公司;4. 北京嘉和无限科技有限公司
  • 出版日期:2020-01-02 发布日期:2020-01-02
  • 基金资助:
    中国石油新疆油田公司科研项目“油气田大数据技术研究与应用”(编号:2017-1.3)。

Analysis on Development Direction of Artificial Intelligence in Oil and Gas Upstream Area

Jia Lu1, Niu Zhijie2, Shi Guowei3,Li Sixu1,Lin Daoshou4   

  1. 1. Information Research Institute of Data Company, PetroChina Xinjiang Oilfield Company, Karamay 834000, China; 2. Exploration Branch of Research Institute of Exploration and Development, PetroChina Xinjiang Oilfield Company, Karamay 834099, China; 3. Data Company, PetroChina Xinjiang Oilfield Company, Karamay 834000, China; 4. Beijing Jiahewuxian Science & Technology Co., Ltd., Beijing 100192, China
  • Online:2020-01-02 Published:2020-01-02
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摘要: 对人工智能本质的认识及其在行业应用的方向性探析非常重要,它可以让我们少走弯路,避 免满目性地开展人工智能应用,减少不必要的资源投入和浪费。文章从业务、专业、数据类型3 个不同角 度分析了油气大数据的构成,从物探、测井、钻录井、油气生产、油藏模拟等专业领域分别探讨了智能化 的发展方向。针对油气综合研究领域,探索并创建了基于大数据分析平台的“勘探有利目标区优选智能分 析系统”,应用多专业、多学科的综合业务智能化分析方法,独创勘探有利目标区标注规则和自动标注方 法,实现了有利目标区的自动分级、可视化展示、查询等功能,可大幅提高用户工作效率。

 

关键词: 油气上游, 大数据, 构成分析, 智能化, 专业方向, 勘探有利目标区

Abstract: It is important to study the nature of artificial intelligence and analyze direction of its industrial application. It can keep us from detours and mistakes, blind application of artificial intelligence and unnecessary investment of resources. This paper analyzes the structure of oil and gas big data from the three different angles of business, specialization and data types. It also focuses on development direction of artificial intelligence in the areas of geophysical exploration, logging, drilling and log, oil and gas production, and oil reservoir simulation. In the light of the comprehensive oil and gas research area, the “smart analysis system for selecting favorable exploration target areas” is created on the basis of the big data analysis platform. The multi-disciplinary and comprehensive artificial intelligence analysis methods are used to uniquely create the marking rules and automatic marking methods for favorable exploration target areas, acquiring a series of functions, such as automatic classification of favorable target areas, visualized display and query, and dramatically improving users’ working efficiency.

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