基于云端大数据的智能导向钻井技术方法

底青云 李守定 付长民 吴思源 王啸天

底青云,李守定,付长民,等. 2021.基于云端大数据的智能导向钻井技术方法[J].工程地质学报,29(1):1178-1185. doi:10.13544/j.cnki.jeg.2021-0055 doi: 10.13544/j.cnki.jeg.2021-0055
引用本文: 底青云,李守定,付长民,等. 2021.基于云端大数据的智能导向钻井技术方法[J].工程地质学报,29(1):1178-1185. doi:10.13544/j.cnki.jeg.2021-0055 doi: 10.13544/j.cnki.jeg.2021-0055
Di Qingyun, Li Shouding, Fu Changmin, et al. 2021. Intelligent steering drilling technology method based on cloud big data[J]. Journal of Engineering Geology, 29(1): 1178-1185. doi: 10.13544/j.cnki.jeg.2021-0055
Citation: Di Qingyun, Li Shouding, Fu Changmin, et al. 2021. Intelligent steering drilling technology method based on cloud big data[J]. Journal of Engineering Geology, 29(1): 1178-1185. doi: 10.13544/j.cnki.jeg.2021-0055

基于云端大数据的智能导向钻井技术方法

doi: 10.13544/j.cnki.jeg.2021-0055
基金项目: 

中国科学院战略性先导科技专项(A类) XDA14040401

中国科学院战略性先导科技专项(A类) XDA14050100

中国科学院战略性先导科技专项(A类) XDA14050300

中国科学院科研仪器设备研制项目 YJKYYQ20190043

中国科学院地质与地球物理研究所重点部署项目 IGGCAS-201903

中国科学院地质与地球物理研究所重点部署项目 SZJJ201901

详细信息
    作者简介:

    底青云(1964-),女,博士,研究员,主要从事地球电磁学理论与应用研究. E-mail: qydi@mail.iggcas.ac.cn

    通讯作者:

    李守定(1979-),男,博士,正高级工程师,主要从事工程地质力学研究. E-mail: lsdlyh@mail.iggcas.ac.cn

  • 中图分类号: P642

INTELLIGENT STEERING DRILLING TECHNOLOGY METHOD BASED ON CLOUD BIG DATA

Funds: 

the Strategic Priority Research Program of the Chinese Academy of Sciences(A) XDA14040401

the Strategic Priority Research Program of the Chinese Academy of Sciences(A) XDA14050100

the Strategic Priority Research Program of the Chinese Academy of Sciences(A) XDA14050300

Scientific Instrument Developing Project of the Chinese Academy of Sciences YJKYYQ20190043

the Key Deployment Program of the Chinese Academy of Sciences IGGCAS-201903

the Key Deployment Program of the Chinese Academy of Sciences SZJJ201901

  • 摘要: 导向钻井技术方法是21世纪全球石油工业最重要的技术之一,也是美国“页岩气革命”核心技术水平钻井的关键组成部分。当前,导向钻井的主要研究目标是提高钻井速度、降低钻井时间和风险,智能化是目标实现的重要途径。文章分析了国内外大数据与人工智能在石油工业应用情况,建立了云端大数据智能导向钻井方法架构,提出了随钻测井参数人工智能反演与识别方法,指出了云端大数据与智能算法管理的实现途径,得出如下结论:(1)基于云端大数据智能导向钻井方法主要包括物联网感知层、大数据存储层和云平台决策层。物联网感知层实现井场关键信息的采集并传输至大数据中心;大数据中心支持数据存储与云管理;云平台决策层依托大数据中心的海量数据,进行云端地面软件控制、人工智能决策以及云平台管理。(2)采用机器学习的方法智能反演与识别地层岩性,选择自然电位、自然伽马、密度、声波、补偿中子、电阻率等6条随钻测井数据,分别采用不同的机器学习算法进行地层岩性反演与识别,决策树模型和随机森林模型分别达到0.81和0.89的准确度,形成了一套可快速自动描述岩性特性分类的方案。(3)云端平台管理决策可进行井下实时数据解码,获取钻井轨迹和测井曲线,其中云端人工智能决策模块对地层及钻井参数进行智能反演预测,可实现钻井轨迹智能修正和钻井参数智能优化,保证智能导向工程钻得准、钻得快。
  • 图  1  智能导向云平台架构

    Figure  1.  Intelligent-oriented cloud platform architecture

    图  2  云平台数据流

    Figure  2.  Cloud platform data flow

    图  3  研究路线

    Figure  3.  Research route

    图  4  测井参数与岩性分布情况

    Figure  4.  Logging parameters and lithology distribution

    图  5  决策树模型

    Figure  5.  Decision tree model

    图  6  预测值与真实值对比

    Figure  6.  Comparison of predicted value and true value

    图  7  随机森林模型的学习曲线

    Figure  7.  The learning curve of the random forest model

    表  1  岩性分类表

    Table  1.   Lithology classification table

    岩性名称 类别
    褐色含泥细砂岩 1
    褐色泥砾状砂岩 2
    褐色泥岩 3
    褐色细砂岩 4
    褐色中砂岩 5
    灰色细砂岩 6
    灰色中砂岩 7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-01-31
  • 修回日期:  2021-02-08
  • 刊出日期:  2021-02-01

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