Drilling Intelligent Decision Support System Based on Big Data and Fusion Model
-
摘要:
为了深度利用钻井过程中产生的大量数据，实现对随钻风险监测与预警的智能分析和辅助决策，基于C/S三层架构和数据中台，结合物理模型、智能算法和趋势分析技术，开发了钻井智能辅助决策系统。通过分析钻井数据来源、结构和用途，结合数据传输、自然语言提取和数据融合技术，实现了多源异构数据获取、融合和管理；综合考虑钻井过程中水力学和管柱力学的耦合影响，设计了模型融合机制，建立了随钻数字井筒系统。在此基础上，结合预测参数与实测参数的偏差变化趋势，建立了风险异常监测算法，将针对井下故障和复杂情况的处理措施与预警机制相结合，实现了风险预警与辅助决策。该系统在页岩油气水平井、深井等不同类型探井中应用50余口井，预警结果与现场符合率达91.5%，验证了其可行性及实用性。钻井智能辅助决策系统能够进行钻井参数优化和钻井风险监测，为高效安全钻井提供了技术保障。
Abstract:In order to deeply leverage the large amount of data generated during the drilling process and achieve intelligent analysis and auxiliary decision-making for risk monitoring while drilling, a drilling intelligent decision support system has been developed. This system was developed based on a client/server (C/S) three-tier architecture and data platform, integrating with the physical models, intelligent algorithms, and trend analysis technologies. Through analyzing the source, structure, and usage of drilling data, combined with data transmission, natural language extraction, and data fusion technology, the data acquisition, fusion, and management of multi-source heterogeneous data have been achieved. Taking into account the coupling effect of hydraulics and tubular mechanics during the drilling process, the model fusion mechanism was designed, and the digital wellbore system while drilling was established. On this basis, combined with the deviation trend of predicted parameters and measured parameters, a risk anomaly monitoring algorithm was established, and thus the risk alerting and decision-making support could be realized through integrating construction measures with the alerting mechanism for downhole failure and complex situations. This system has been applied in over 50 exploration wells across various types, including shale oil and gas horizontal wells and deep wells, with an accuracy rate of 91.5% between predicted results and actual field results. Its practicality and feasibility was thereby verified. The drilling intelligent decision support system can realize drilling parameter optimization and risk monitoring, thereby it serves as a robust technical safeguard for the efficient drilling and safe operations.
-
Keywords:
- big data /
- fusion model /
- intelligent decision-making /
- decision support /
- risk alerting /
- intelligent drilling
-
-
![]()
图 1 钻井智能辅助决策系统架构设计示意
Figure 1. Architecture design of drilling intelligent decision support system
![]()
图 2 钻井智能辅助决策系统数据库的建立过程
Figure 2. Process of establishing database for drilling intelligent decision support system
![]()
图 7 滑动钻进工况下模型和数据流的关系
Figure 7. Relationship between model and data flow under sliding drilling conditions
![]()
图 13 川渝页岩气井（B井）卡钻故障回顾性分析结果
Figure 13. Retrospective analysis results of sticking in Well B of Sichuan-Chongqing shale gas
-
[1] 李根生,宋先知,祝兆鹏,等. 智能钻完井技术研究进展与前景展望[J]. 石油钻探技术,2023,51(4):35–47. doi: 10.11911/syztjs.2023040 LI Gensheng, SONG Xianzhi, ZHU Zhaopeng, et al. Research progress and the prospect of intelligent drilling and completion technologies[J]. Petroleum Drilling Techniques, 2023, 51(4): 35–47. doi: 10.11911/syztjs.2023040
[2] 杨传书,李昌盛,孙旭东,等. 人工智能钻井技术研究方法及其实践[J]. 石油钻探技术,2021,49(5):7–13. doi: 10.11911/syztjs.2020136 YANG Chuanshu, LI Changsheng, SUN Xudong, et al. Research method and practice of artificial intelligence drilling technology[J]. Petroleum Drilling Techniques, 2021, 49(5): 7–13. doi: 10.11911/syztjs.2020136
[3] 张菲菲,王茜,王学迎,等. 油气井工程多源多模态数据融合技术与展望[J]. 天然气工业,2024,44(9):152–166. ZHANG Feifei, WANG Xi, WANG Xueying, et al. Multi-source and multi-modal data fusion technology and its prospect in oil and gas well engineering[J]. Natural Gas Industry, 2024, 44(9): 152–166.
[4] 耿黎东. 大数据技术在石油工程中的应用现状与发展建议[J]. 石油钻探技术,2021,49(2):72–78. doi: 10.11911/syztjs.2020134 GENG Lidong. Application status and development suggestions of big data technology in petroleum engineering[J]. Petroleum Drilling Techniques, 2021, 49(2): 72–78. doi: 10.11911/syztjs.2020134
[5] 张福祥,郑新权,李志斌,等. 钻井优化系统在国内非常规油气资源开发中的实践[J]. 中国石油勘探,2020,25(2):96–109. doi: 10.3969/j.issn.1672-7703.2020.02.010 ZHANG Fuxiang, ZHENG Xinquan, LI Zhibin, et al. Practice of drilling optimization system in the development of unconventional oil and gas resources in China[J]. China Petroleum Exploration, 2020, 25(2): 96–109. doi: 10.3969/j.issn.1672-7703.2020.02.010
[6] XIE Tao, HOU Xinxin, HUO Hongbo, et al. Improving drilling efficiency using intelligent decision system for drilling in Bohai Oilfield based on big data[R]. SPE 215427, 2023.
[7] 林伯韬,郭建成. 人工智能在石油工业中的应用现状探讨[J]. 石油科学通报,2019,4(4):403–413. LIN Botao, GUO Jiancheng. Discussion on current application of artificial intelligence in petroleum industry[J]. Petroleum Science Bulletin, 2019, 4(4): 403–413.
[8] 马涛,张仲宏,王铁成,等. 勘探开发梦想云平台架构设计与实现[J]. 中国石油勘探,2020,25(5):71–81. doi: 10.3969/j.issn.1672-7703.2020.05.010 MA Tao, ZHANG Zhonghong, WANG Tiecheng, et al. Architecture design and implementation of E & P Dream Cloud Platform[J]. China Petroleum Exploration, 2020, 25(5): 71–81. doi: 10.3969/j.issn.1672-7703.2020.05.010
[9] 雷智博,唐梦,宋泽文. 工程作业智能支持中心 (EISC) 建设与应用[J]. 信息系统工程,2022(7):121–124. doi: 10.3969/j.issn.1001-2362.2022.07.032 LEI Zhibo, TANG Meng, SONG Zewen. Construction and application of engineering operation intelligent support center (EISC)[J]. China CIO News, 2022(7): 121–124. doi: 10.3969/j.issn.1001-2362.2022.07.032
[10] 柳海啸,刘芳,代文星,等. 基于大数据分析技术的钻井提效实践[J]. 石油钻采工艺,2021,43(4):436–441. LIU Haixiao, LIU Fang, DAI Wenxing, et al. Drilling efficiency improvement practice based on big data analysis technology[J]. Oil Drilling & Production Technology, 2021, 43(4): 436–441.
[11] 刘宝生,和鹏飞,杨保健,等. 钻井辅助决策系统构建及在渤中19-6的工程实践[J]. 石油钻采工艺,2018,40(6):684–689. LIU Baosheng, HE Pengfei, YANG Baojian, et al. Construction of assistant decision-making system for well drilling and its engineering practice in BZ19-6 Block[J]. Oil Drilling & Production Technology, 2018, 40(6): 684–689.
[12] BELLO B, HOLZMANN J, YAQOOB T, et al. Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art[J]. Journal of Artificial Intelligence and Soft Computing Research, 2015, 5(2): 121–139. doi: 10.1515/jaiscr-2015-0024
[13] 曾义金,李大奇,陈曾伟,等. 基于自然语言处理与大数据分析的漏失分析与诊断[J]. 石油钻探技术,2023,51(6):1–11. doi: 10.11911/syztjs.2023069 ZENG Yijin, LI Daqi, CHEN Zengwei, et al. Loss analysis and diagnosis based on natural language processing and big data analysis[J]. Petroleum Drilling Techniques, 2023, 51(6): 1–11. doi: 10.11911/syztjs.2023069
[14] 张菲菲,崔亚辉,于琛,等. 基于机器学习的钻井工况识别技术现状及发展[J]. 长江大学学报(自然科学版),2023,20(4):53–65. doi: 10.3969/j.issn.1673-1409.2023.04.006 ZHANG Feifei, CUI Yahui, YU Chen, et al. Recent developments and future trends of drilling status recognition technology based on machine learning[J]. Journal of Yangtze University (Natural Science Edition), 2023, 20(4): 53–65. doi: 10.3969/j.issn.1673-1409.2023.04.006
[15] CAO Dingzhou, HENDER D, ARIABOD S, et al. Digital transformation strategy enables automated real-time torque-and-drag modeling[R]. SPE 199670, 2020.
[16] 李紫璇,张菲菲,祝钰明,等. 钻井模型与机器学习耦合的实时卡钻预警技术[J]. 石油机械,2022,50(4):15–21. LI Zixuan, ZHANG Feifei, ZHU Yuming, et al. Real-time pipe sticking early warning technology based on coupling of drilling model and machine learning[J]. China Petroleum Machinery, 2022, 50(4): 15–21.
[17] 王茜,张菲菲,李紫璇,等. 基于钻井模型与人工智能相耦合的实时智能钻井监测技术[J]. 石油钻采工艺,2020,42(1):6–15. WANG Xi, ZHANG Feifei, LI Zixuan, et al. Real-time intelligent drilling monitoring technique based on the coupling of drilling model and artificial intelligence[J]. Oil Drilling & Production Technology, 2020, 42(1): 6–15.
-
期刊类型引用(2)
1. 李玉民. 气体钻井用Φ139.7 mm钻杆DS55螺纹接头断裂原因分析. 石油管材与仪器. 2022(04): 85-89 . 
百度学术
2. 郭王恒，吴百川. 极限工况下钻杆接头疲劳分析. 机械制造. 2020(03): 41-45 . 百度学术
其他类型引用(1)
下载:
计量
- 文章访问数: 356
- HTML全文浏览量: 58
- PDF下载量: 165
- 被引次数: 3
