Progress and Prospects of Sinopec’s Intelligent Drilling Technologies
-
摘要:
智能钻井具有自感知、自学习、自决策、自执行和自适应等功能，有望大幅度提高钻井效率，降低钻井成本。为加速推动智能钻井技术发展，中国石化针对实时感知、智能决策及集成控制等方面存在的关键技术难点，开展了自动化钻机及关键装备、随钻工程地质参数感知、智能钻井分析决策和智能钻井系统集成等的攻关研究，进行了现场集成与示范应用，实现了钻井参数智能优化、井筒风险智能预警、井眼轨迹智能导航等应用场景的多目标协同优化，达到了咨询模式下智能辅助−人工决策的闭环控制水平，支撑了重点油气领域勘探开发的降本增效。为更好地发展智能钻井技术，在分析所存在问题的基础上，梳理、总结了中国石化近些年在智能钻井关键技术研究取得的进展，提出应进一步加强全自动钻机装备、高性能测传控系统和数字孪生决策系统等关键技术研究，加大技术迭代升级，推动智能钻井由咨询模式向半自主、自主控制模式转变。
Abstract:Intelligent drilling has functions such as self-perception, self-learning, self-decision-making, self-execution, and self-adaptation. It is expected to significantly improve drilling efficiency and reduce operating costs. In order to accelerate the development of intelligent drilling technologies, Sinopec has conducted research on key technologies such as automated drilling rigs and key equipment, geological parameter perception while drilling engineering, intelligent drilling analysis and decision-making, and intelligent drilling system integration in terms of real-time perception, intelligent decision-making, integrated control, and other aspects. Integration and demonstration applications have been carried out on site, achieving multi-objective collaborative optimization of application scenarios such as intelligent optimization of drilling parameters, intelligent warning of wellbore risks, and intelligent navigation of wellbore trajectories. The closed-loop control level of intelligent assistance and manual decision-making in consulting mode has been achieved, which effectively supports cost reduction and efficiency improvement in key oil and gas exploration and development fields. In order to better develop intelligent drilling technologies, based on the analysis of the current problems, the latest progress in the research on key technologies for intelligent drilling in Sinopec was summarized. It was proposed to further strengthen the research on key technologies such as fully automated drilling rigs and equipment, high-performance measurement and control systems, and digital twin decision-making systems, increase technological iteration and upgrading, and promote the transformation of intelligent drilling from consulting mode to semi-autonomous and autonomous control modes.
-
-
![]()
图 2 3D视觉引导实现料袋识别与抓取
Figure 2. 3D visual guidance for material recognition and grasping
![]()
图 3 随钻高分辨率电阻率成像仪
Figure 3. High-resolution resistivity imaging instrument while drilling
![]()
图 4 耐温175 ℃随钻井下动态工程参数测量仪
Figure 4. Downhole dynamic engineering parameter measurement instrument while drilling with temperature resistance of 175 ℃
![]()
图 5 智能钻井分析决策云平台架构
Figure 5. Architecture of intelligent drilling analysis and decision-making platform
![]()
图 6 多约束条件下钻井参数智能优化调整对钻井的影响
Figure 6. Influence of intelligent optimization and adjustment of drilling parameters on drilling under multiple constraints
![]()
图 8 地层实时预测与井眼轨迹智能导航
Figure 8. Real-time geological prediction and intelligent navigation of wellbore trajectory
表 1 智能钻井发展阶段划分
Table 1 Classification of development stages of intelligent drilling
发展阶段 水平 实现功能 观察模式 L0 不提供任何建议，仅提供井场监测系统、井下信息获取系统、智能报警系统等获取的信息 咨询模式 L1 提供咨询建议，司钻做出决定并执行，如钻井动态过程诊断系统、定向钻井辅助系统等 半自主控制模式 L2 司钻批准建议后，系统自动执行，如自动送钻系统、钻柱黏滑振动地面控制系统、自动控压钻井系统等 自主控制
模式L3 机器自动决策并执行，一般用于某个具体流程，如随钻测量+旋转导向系统等 
下载: 导出CSV
-
[1] 王敏生,光新军. 智能钻井技术现状与发展方向[J]. 石油学报,2020,41(4):505–512. doi: 10.7623/syxb202004013 WANG Minsheng, GUANG Xinjun. Status and development trends of intelligent drilling technology[J]. Acta Petrolei Sinica, 2020, 41(4): 505–512. doi: 10.7623/syxb202004013
[2] 王敏生. 沿钻柱测量技术及其发展方向[J]. 石油钻探技术,2022,50(4):52–58. doi: 10.11911/syztjs.2022001 WANG Minsheng. Along-string measuring technique and its development direction[J]. Petroleum Drilling Techniques, 2022, 50(4): 52–58. doi: 10.11911/syztjs.2022001
[3] 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
[4] 张好林,杨传书,李昌盛,等. 钻井数字孪生系统设计与研发实践[J]. 石油钻探技术,2023,51(3):58–65. doi: 10.11911/syztjs.2023011 ZHANG Haolin, YANG Chuanshu, LI Changsheng, et al. Design and research practice of a drilling digital twin system[J]. Petroleum Drilling Techniques, 2023, 51(3): 58–65. doi: 10.11911/syztjs.2023011
[5] 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
[6] OLIVIER G. Using an E&P digital twin in well construction[EB/OL]. (2022-01-07)[2024-08-12]. https://www.halliburton.com/en/software/decisionspace-365-enterprise/decisionspace-365-well-construction.
[7] BA S, IGNOVA M, MANTLE K, et al. Autonomous directional drilling planning and execution using an industry 4.0 platform[R]. SPE 204607, 2021.
[8] LAI S W, NG J, EDDY A, et al. Large-scale deployment of a closed-loop drilling optimization system: implementation and field results[R]. SPE 199601, 2020.
[9] 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
[10] ZHANG Hongbao, ZENG Yijin, LIAO Lulu, et al. How to land modern data science in petroleum engineering[R]. SPE 205689, 2021.
[11] MAYANI M G, BAYBOLOV T, ROMMETVEIT R, et al. Optimizing drilling wells and increasing the operation efficiency using digital twin technology[R]. SPE 199566, 2020.
[12] KARPOV R, KOJADINOVIC K, KOKOTOVIC U, et al. Live digital twin improving drilling performance and providing foundation for the realtime data quality control[R]. SPE 216981, 2023.
[13] 蔡亚琳,柯式镇,康正明,等. 随钻电阻率成像测井在裂缝地层中的响应模拟[J]. 石油科学通报,2020,5(3):327–336. doi: 10.3969/j.issn.2096-1693.2020.03.028 CAI Yalin, KE Shizhen, KANG Zhengming, et al. Logging response simulation of a LWD resistivity imaging tool in fractured formations[J]. Petroleum Science Bulletin, 2020, 5(3): 327–336. doi: 10.3969/j.issn.2096-1693.2020.03.028
[14] 康正明,柯式镇,李新,等. 随钻电阻率成像测井仪定量评价地层界面探究[J]. 石油钻探技术,2020,48(4):124–130. doi: 10.11911/syztjs.2020087 KANG Zhengming, KE Shizhen, LI Xin, et al. Probe into quantitative stratigraphic interface evaluation using a resistivity imaging LWD tool[J]. Petroleum Drilling Techniques, 2020, 48(4): 124–130. doi: 10.11911/syztjs.2020087
[15] 李新,倪卫宁,米金泰,等. 一种基于非接触耦合原理的新型随钻微电阻率成像仪器[J]. 中国石油大学学报(自然科学版),2020,44(6):46–52. doi: 10.3969/j.issn.1673-5005.2020.06.006 LI Xin, NI Weining, MI Jintai, et al. A novel high-resolution resistivity imaging while drilling tool based on contactless coupling[J]. Journal of China University of Petroleum(Edition of Natural Science), 2020, 44(6): 46–52. doi: 10.3969/j.issn.1673-5005.2020.06.006
[16] KANG Zhengming, LI Xin, NI Weining, et al. Using logging while drilling resistivity imaging data to quantitatively evaluate fracture aperture based on numerical simulation[J]. Journal of Geophysics and Engineering, 2021, 18(3): 317–327. doi: 10.1093/jge/gxab016
[17] XU Wei, HUANG Hang, LI Xin, et al. Self-adaptive inversion method of electromagnetic-propagation resistivity logging while drilling data[J]. Applied Geophysics, 2022, 19(3): 343–352. doi: 10.1007/s11770-022-0939-1
[18] ZHANG Hongbao, LU Baoping, YANG Shunhui, et al. A global drilling KPIs analysis system based on modern data science techniques[R]. SPE 203378, 2020.
[19] ZHANG Hongbao, LU Baoping, LIAO Lulu, et al. Combining machine learning and classic drilling theories to improve rate of penetration prediction[R]. SPE 202202, 2021.
[20] ZHANG Hongbao, ZENG Yijin, BAO Hongzhi, et al. Drilling and completion anomaly detection in daily reports by deep learning and natural language processing techniques[R]. URTEC 2020-2885, 2020.
[21] ZHOU Fei, FAN Honghai, LU Baoping, et al. Application of DNN-TCN composite neural network in rate of penetration prediction[R]. SPE 209886, 2022.
[22] BAI Kankan, FAN Honghai, ZHANG Hongbao, et al. Real time torque and drag analysis by combining of physical model and machine learning method[R]. URTEC 3723045, 2022.
[23] ZHOU Fei, FAN Honghai, LIU Yuhan, et al. Application of XGBoost algorithm in rate of penetration prediction with accuracy[R]. IPTC 22100, 2022.
[24] LU Baoping, XU Ting, HUANG Yuebin, et al. Applications of computer vision and deep learning in visual features extraction of drill bits[R]. IPTC 22624, 2022.
[25] 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
[26] LU Baoping, YUAN Duo, WU Chao, et al. A drilling technology guided by well-seismic information integration[J]. Petroleum Exploration and Development, 2020, 47(6): 1325–1332. doi: 10.1016/S1876-3804(20)60140-4
[27] JIANG Jun, LUO Fang, ZHANG Hongbao, et al. Adaptive multiexpert learning for lithology recognition[J]. SPE Journal, 2022, 27(6): 3802–3813. doi: 10.2118/209824-PA
-
期刊类型引用(1)
1. 王军磊，位云生，曹正林，陈东，唐海发. 基于相渗滞后效应的水驱裂缝性气藏注N_2提高天然气采收率机理. 天然气工业. 2025(03): 96-111 . 
百度学术
其他类型引用(0)
计量
- 文章访问数: 675
- HTML全文浏览量: 133
- PDF下载量: 285
- 被引次数: 1
