Architecture of a Digital Twin-Based Adaptive Control System for Drilling Parameters
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摘要:
智能化钻井背景下，传统的司钻调控钻进参数的钻井模式已经无法满足钻进参数自适应调控的需求，而数字孪生技术具有实时同步、真实映射和高保真度的特性，因而构建钻进参数自适应调控数字孪生系统，与智能钻井设备、智能终端相结合，可实现钻机自主送钻和自主定向。为此 ，在介绍钻进参数自适应调控技术架构及其工艺流程的基础上，设计了基于数字孪生技术的钻进参数自适应调控系统架构，包括物理钻井平台、虚拟钻井平台、钻井孪生数据、服务应用层和通信连接层5个要素；并从数字孪生系统的构建、演化及更新等３个方面阐述了钻进参数自适应调控数字孪生系统的运行机制，分析了关键理论技术需求。研究表明，钻进参数自适应调控数字孪生系统能够实现钻进参数自适应调控技术和智能钻机、智能设备的深度融合，推动数字孪生技术在钻井工程中的应用，对于实现智能化钻井有重要作用。
Abstract:Under the background of intelligent drilling, the traditional drilling mode in which the drilling parameters are controlled by the driller fails to meet the needs of adaptive control of drilling parameters. Because the digital twin technology has the characteristics of real-time synchronization, faithful mapping, and high fidelity, a digital twin-based adaptive control system for drilling parameters was constructed, which combined with intelligent drilling equipment and intelligent terminal, so as to realize autonomous bit feed and autonomous orientation of the drilling rig. Therefore, the technical architecture of adaptive control of drilling parameters and its process flow was introduced, and the architecture of a digital twin-based adaptive control system for drilling parameters was designed, including five components: physical drilling platform, virtual drilling platform, drilling twin data, service application layer, and communication connection layer. The operational mechanism of the digital twin-based adaptive control system for drilling parameters was discussed from three aspects: the construction, evolution, and updates of the digital twin system. Furthermore, the key theoretical and technical requirements were analyzed. The research shows that the digital twin-based adaptive control system for drilling parameters integrates adaptive control technology for drilling parameters with intelligent drilling rigs and equipment, promoting the application of digital twin technology in drilling engineering and significantly contributing to the realization of intelligent drilling.
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图 1 钻进参数自适应调控技术架构
Figure 1. Architecture of adaptive control technology for drilling parameters
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图 2 钻进参数自适应调控技术的工艺流程
Figure 2. Process flow of adaptive control technology for drilling parameters
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图 3 钻进参数自适应调控数字孪生系统架构
Figure 3. Architecture of digital twin-based adaptive control system for drilling parameters
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图 4 钻进参数自适应调控数字孪生系统运行机制
Figure 4. Operational mechanism of digital twin-based adaptive control system for drilling parameters
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[1] 陶飞,刘蔚然,张萌,等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统,2019,25(1):1–18. TAO Fei, LIU Weiran, ZHANG Meng, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1–18.
[2] 陶飞,刘蔚然,刘检华,等. 数字孪生及其应用探索[J]. 计算机集成制造系统,2018,24(1):1–18. TAO Fei, LIU Weiran, LIU Jianhua, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1–18.
[3] 杨林瑶,陈思远,王晓,等. 数字孪生与平行系统:发展现状、对比及展望[J]. 自动化学报,2019,45(11):2001–2031. YANG Linyao, CHEN Siyuan, WANG Xiao, et al. Digital twins and parallel systems: state of the art, comparisons and prospect[J]. Acta Automatica Sinica, 2019, 45(11): 2001–2031.
[4] 李伯虎,柴旭东,张霖,等. 面向新型人工智能系统的建模与仿真技术初步研究[J]. 系统仿真学报,2018,30(2):349–362. LI Bohu, CHAI Xudong, ZHANG Lin, et al. Preliminary study of modeling and simulation technology oriented to neo-type artificial intelligent systems[J]. Journal of System Simulation, 2018, 30(2): 349–362.
[5] 闫铁,许瑞,刘维凯,等. 中国智能化钻井技术研究发展[J]. 东北石油大学学报,2020,44(4):15–21. doi: 10.3969/j.issn.2095-4107.2020.04.003 YAN Tie, XU Rui, LIU Weikai, et al. Research and development of intelligent drilling technology in China[J]. Journal of Northeast Petroleum University, 2020, 44(4): 15–21. doi: 10.3969/j.issn.2095-4107.2020.04.003
[6] 杨传书,张好林,肖莉. 自动化钻井关键技术进展与发展趋势[J]. 石油机械,2017,45(5):10–17. YANG Chuanshu, ZHANG Haolin, XIAO Li. Key technical progress and development trend of automated drilling[J]. China Petroleum Machinery, 2017, 45(5): 10–17.
[7] 姜海龙,柳贡慧,李军,等. 井下工程参数测量系统的研制与应用[J]. 钻采工艺,2020,43(1):5–8. doi: 10.3969/J.ISSN.1006-768X.2020.01.02 JIANG Hailong, LIU Gonghui, LI Jun, et al. Development and application of downhole engineering data measurement system[J]. Drilling & Production Technology, 2020, 43(1): 5–8. doi: 10.3969/J.ISSN.1006-768X.2020.01.02
[8] 李根生,宋先知,田守嶒. 智能钻井技术研究现状及发展趋势[J]. 石油钻探技术,2020,48(1):1–8. doi: 10.11911/syztjs.2020001 LI Gensheng, SONG Xianzhi, TIAN Shouceng. Intelligent drilling technology research status and development trends[J]. Petroleum Drilling Techniques, 2020, 48(1): 1–8. doi: 10.11911/syztjs.2020001
[9] CHAPMAN C D, FLORES J L, de LEON PEREZ R, et al. Automated closed-loop drilling with ROP optimization algorithm significantly reduces drilling time and improves downhole tool reliability[R]. SPE 151736, 2012.
[10] LAI S W, NG J, EDDY A, et al. Large-scale deployment of a closed-loop drilling optimization system: Implementation and field results[J]. SPE Drilling & Completion, 2021, 36(1): 47–62.
[11] MAHMOUD H, HAMZA A, NASSER M S, et al. Hole cleaning and drilling fluid sweeps in horizontal and deviated wells: comprehensive review[J]. Journal of Petroleum Science and Engineering, 2020, 186: 106748. doi: 10.1016/j.petrol.2019.106748
[12] ZHU Xiaohua, LI Ke, AN Jiawei. Calculation and analysis of dynamic drag and torque of horizontal well strings[J]. Natural Gas Industry B, 2019, 6(2): 183–190. doi: 10.1016/j.ngib.2018.08.004
[13] AKGUN F. A finite element model for analyzing horizontal well BHA behavior[J]. Journal of Petroleum Science and Engineering, 2004, 42(2/4): 121–132.
[14] WU Xiang, ZHANG Kanjian. Three-dimensional trajectory design for horizontal well based on optimal switching algorithms[J]. ISA Transactions, 2015, 58: 348–356. doi: 10.1016/j.isatra.2015.04.002
[15] NAJJARPOUR M, JALALIFAR H, NOROUZI-APOURVARI S. Half a century experience in rate of penetration management: application of machine learning methods and optimization algorithms: a review[J]. Journal of Petroleum Science and Engineering, 2022, 208(part D): 109575.
[16] KUANG Yuchun, LIN Wei, LIU Yang, et al. Numerical modelling and field experimental validation of the axial load transfer on the drill-strings in deviated wells[J]. Journal of Natural Gas Science and Engineering, 2020, 75: 103124. doi: 10.1016/j.jngse.2019.103124
[17] DEPOUHON A, DETOURNAY E. Instability regimes and self-excited vibrations in deep drilling systems[J]. Journal of Sound and Vibration, 2014, 333(7): 2019–2039. doi: 10.1016/j.jsv.2013.10.005
[18] 孙晓峰,姚笛,孙士慧,等. 基于漂移流动模型的水平井岩屑床高度瞬态计算新方法[J]. 天然气工业,2022,42(5):85–92. doi: 10.3787/j.issn.1000-0976.2022.05.009 SUN Xiaofeng, YAO Di, SUN Shihui, et al. A new transient calculation method of cuttings bed thickness based on drift flow model[J]. Natural Gas Industry, 2022, 42(5): 85–92. doi: 10.3787/j.issn.1000-0976.2022.05.009
[19] RASHIDI B, HARELAND G, NYGAARD R. Real-time drill bit wear prediction by combining rock energy and drilling strength concepts[R]. SPE 117109, 2008.
[20] WIDMER G, KUBAT M. Learning in the presence of concept drift and hidden contexts[J]. Machine Learning, 1996, 23(1): 69–101.
[21] 高德利,刘维,万绪新,等. PDC钻头钻井提速关键影响因素研究[J]. 石油钻探技术,2023,51(4):20–34. doi: 10.11911/syztjs.2023022 GAO Deli, LIU Wei, WAN Xuxin, et al. Study on key factors influencing the ROP improvement of PDC bits[J]. Petroleum Drilling Techniques, 2023, 51(4): 20–34. doi: 10.11911/syztjs.2023022
[22] 刘湘华,杜欢,刘彪,等. 顺北Ⅳ号条带超深高温定向井钻井关键技术[J]. 石油钻采工艺,2022,44(6):665–670. LIU Xianghua, DU Huan, LIU Biao, et al. Key technology of directional drilling in the ultra-deep high-temperature IV belt, the Shunbei Oilfield[J]. Oil Drilling & Production Technology, 2022, 44(6): 665–670.
[23] GILLAN C, BOONE S, KOSTIUK G, et al. Applying precision drill pipe rotation and oscillation to slide drilling problems[R]. SPE 118656, 2009.
[24] 孙明光,张洪宁,刘卫东,等. 特深定向井滑动导向过程中扭矩传递规律研究[J]. 石油机械,2020,48(2):1–8. SUN Mingguang, ZHANG Hongning, LIU Weidong, et al. Torque transmission law in slide steering for extra-deep directional wells[J]. China Petroleum Machinery, 2020, 48(2): 1–8.
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