1/24/2025, 1:36:53 AM 星期五
深部地质钻探钻进过程流式大数据分析与动态预处理——以辽宁丹东3000 m科学钻探工程为例
CSTR:
作者:
作者单位:

1.中国地质大学(武汉)自动化学院,湖北 武汉 430074;2.复杂系统先进控制与智能自动化湖北省重点实验室,湖北 武汉 430074;3.地球探测智能化技术教育部工程研究中心,湖北 武汉 430074;4.山东省第三地质矿产勘查院,山东 烟台 264000

中图分类号:

P634

基金项目:

国家自然科学基金青年项目“基于多源井震信息融合的地质钻进过程钻速智能优化”(编号:62003318);国家自然科学基金面上项目“复杂地质钻进过程效率动态优化与安全智能预警”(编号:62173313);国家自然科学基金重点项目“复杂地质钻进过程智能控制”(编号:61733016);湖北省自然科学基金创新群体项目“地质钻探智能化技术及应用”(编号:2020CFA031);中央高校基本科研业务费专项资金科研项目“考虑复杂地质环境的钻进过程钻速优化”(编号:CUG2106350)


Streaming big data analysis and dynamic pre-processing in deep geological drilling process: A case study on the 3000m scientific drilling project in Dandong, Liaoning province
Author:
  • GAN Chao 1,2,3

    GAN Chao

    School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China
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  • CAO Weihua 1,2,3

    CAO Weihua

    School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China
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  • WANG Luzhao 4

    WANG Luzhao

    No.3 Exploration Institute of Geology and Mineral Resources of Shandong Province,Yantai Shandong 264000, China
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  • WU Min 1,2,3

    WU Min

    School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China
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Affiliation:

1.School of Automation, China University of Geosciences, Wuhan Hubei 430074, China;2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan Hubei 430074, China;3.Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education,Wuhan Hubei 430074, China;4.No.3 Exploration Institute of Geology and Mineral Resources of Shandong Province,Yantai Shandong 264000, China

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    摘要:

    深部地质钻探钻进过程数据价值密度低,传统方法难以在钻中流式大数据条件下有效去除尖峰、毛刺等各类钻进过程数据噪声。本文提出一种深部地质钻探钻进过程流式大数据分析与动态预处理方法,并成功应用于辽宁丹东3000 m科学钻探工程。首先,深入分析过程工艺和数据处理需求,建立深部地质钻探钻进过程流式大数据分析与动态预处理框架结构;然后,运用限幅滤波结合过程数据分布特征、司钻/机长人工操作经验去除过程数据中的离群值;接着,引入滑动窗口策略对流式钻进大数据进行动态处理,在每个窗口中运用Savitzky Golay滤波进一步提升数据质量。仿真实验和工程应用结果验证了本文方法具有很好的工程适用性和有效性。

    Abstract:

    The data quality in deep geological drilling process is poor, and traditional methods are hard to effectively remove all kinds of data noise such as spikes and burrs. A streaming big data analysis and dynamic pre-processing method for deep geological drilling process was proposed and successfully applied to the 3000m scientific drilling project in Dandong, Liaoning province. Firstly, the process mechanism and requirements of data processing are deeply analyzed, and the framework of streaming big data analysis and dynamic pre-processing in deep geological drilling process is established. After that, the outliers in the process data are removed by limiting filtering combined with the distribution characteristics of the process data and the driller’s manual operation experience. Then, the moving window strategy is introduced to dynamically process the big data of convective drilling, and savitzky Golay filter is used in each window to further improve the data quality. Finally, results of simulations and engineering application show that the proposed method has good engineering applicability and effectiveness.

    参考文献
    [1] 甘超.复杂地层可钻性场智能建模与钻速优化[D].武汉:中国地质大学(武汉),2019.GAN Chao. Intelligent modeling of formation drillability field and drilling rate of penetration optimization in complex conditions[D]. Wuhan: China University of Geosciences, 2019.
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    [3] 张正,赖旭芝,陆承达,等.基于贝叶斯网络的钻进过程井漏井涌事故预警[J].探矿工程(岩土钻掘工程),2020,47(4):114-121,144.ZHANG Zheng, LAI Xuzhi, LU Chengda, et al. Lost circulation and kick accidents warning based on Bayesian network for the drilling process[J]. Exploration Engineering (Rock & Soil Drilling and Tunneling), 2020,47(4):114-121.
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甘超,曹卫华,王鲁朝,等.深部地质钻探钻进过程流式大数据分析与动态预处理——以辽宁丹东3000 m科学钻探工程为例[J].钻探工程,2022,49(4):1-7.
GAN Chao, CAO Weihua, WANG Luzhao, et al. Streaming big data analysis and dynamic pre-processing in deep geological drilling process: A case study on the 3000m scientific drilling project in Dandong, Liaoning province[J]. Drilling Engineering, 2022,49(4):1-7.

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历史
  • 收稿日期:2022-05-05
  • 最后修改日期:2022-06-10
  • 录用日期:2022-06-12
  • 在线发布日期: 2022-07-18
  • 出版日期: 2022-07-10
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