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人工智能在压裂改造技术中的应用现状及前景展望

张世昆 陈作

张世昆,陈作. 人工智能在压裂改造技术中的应用现状及前景展望[J]. 石油钻探技术,2022, 50(6):1-9 doi: 10.11911/syztjs.2022115
引用本文: 张世昆,陈作. 人工智能在压裂改造技术中的应用现状及前景展望[J]. 石油钻探技术,2022, 50(6):1-9 doi: 10.11911/syztjs.2022115
ZHANG Shikun, CHEN Zuo. Application status and prospect of artificial intelligence in reservoir stimulation [J]. Petroleum Drilling Techniques,2022, 50(6):1-9 doi: 10.11911/syztjs.2022115
Citation: ZHANG Shikun, CHEN Zuo. Application status and prospect of artificial intelligence in reservoir stimulation [J]. Petroleum Drilling Techniques,2022, 50(6):1-9 doi: 10.11911/syztjs.2022115

人工智能在压裂改造技术中的应用现状及前景展望

doi: 10.11911/syztjs.2022115
基金项目: 国家重点研发计划项目“干热岩能量获取与利用相关科学问题研究”(编号:2018YFB1501802)、国家自然科学基金项目“海相深层油气富集机理与关键工程技术基础研究”(编号:U19B6003)联合资助
详细信息
    作者简介:

    张世昆(1991—),男,山东平度人,2015年毕业于中国石油大学(北京)石油工程专业,2020年获中国石油大学(北京)油气井工程专业博士学位,助理研究员,主要从事非常规油气储层改造工作。E-mail:zhangsk.sripe@sinopec.com。

  • 中图分类号: TE357.3

Application Status and Prospect of Artificial Intelligence in Reservoir Stimulation

  • 摘要:

    随着人工智能理论和计算机技术的快速发展,智能化和数字化已成为推动储层压裂改造发展的重要力量。针对压裂改造技术智能化发展,阐述了人工智能技术在地质参数预测、压裂参数优化设计、压裂施工实时诊断与调控、压裂工具及材料研发等方面的研究进展与应用情况,分析了当前智能压裂改造技术发展存在的主要问题与今后的重点发展方向,认识到智能压裂改造技术仍处于探索试验阶段,国外在“甜点”智能识别、压裂参数优化、现场施工智能化控制等方面研究已走在前列,并在北美地区多个区块的压裂服务中成功应用,国内仅在压裂大数据机器学习、智能化压裂材料等方面进行了早期探索,在智能压裂装备、工具、实时监测诊断、现场智能化调控等方面的研究与应用较少,较国外存在较大差距。指出了数据样本可靠性差、一体化智能压裂方法与装备欠缺和多领域交叉人才缺乏等是影响智能压裂改造技术快速发展的关键问题,并预测随着万物互联技术的发展,将形成智能化完井压裂系统,不需要人工干预即可完成储层评估、“甜点”识别、压裂参数优化设计、现场调控、压后评估等工作,真正实现一体化智能储层改造。

     

  • 图 1  J.Guevara等人建立的智能预测模型流程

    Figure 1.  Overview of the proposed methodology

    图 2  压裂优化预测代理模型的工作流程

    Figure 2.  Generic workflow to build a predictive proxy model for completion optimization

    图 3  循环机器学习预测井口压力的流程

    Figure 3.  The diagram for continuous machine learning

    图 4  SmartFleet一体化压裂系统的工作流程

    Figure 4.  Integrated fracturing system—SmartFleet system form Halliburton

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出版历程
  • 收稿日期:  2021-12-17
  • 修回日期:  2022-10-24
  • 网络出版日期:  2022-11-07

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