基于地层与井筒耦合流动响应特征的溢流早期监测方法

doi:10.7623/syxb202607013

石油学报 ›› 2026, Vol. 47 ›› Issue (7): 1518-1530.DOI: 10.7623/syxb202607013

• 石油工程 • 上一篇

基于地层与井筒耦合流动响应特征的溢流早期监测方法

李昊^1,2, 路建社^1,2, 孙宝江^1,2, 任美鹏³, 王春生⁴, 张耀明⁴, 殷志明³, 李秀彬⁵

1. 深层油气全国重点实验室(中国石油大学(华东)) 山东青岛 266580;
2. 中国石油大学(华东)石油工程学院山东青岛 266580;
3. 中海油研究总院有限责任公司北京 100027;
4. 中国石油塔里木油田公司油气工艺研究院新疆库尔勒 841004;
5. 中国石油集团西部钻探工程有限公司地质研究院新疆克拉玛依 834000

收稿日期:2025-07-16 修回日期:2026-04-28 发布日期:2026-08-04
通讯作者: 孙宝江,男,1963年11月生,1999年获北京大学流体力学专业博士学位,现为中国石油大学(华东)石油工程学院教授,主要从事井筒多相流理论及应用、海洋油气工程领域的研究工作。Email:sunbj1128@vip.126.com
作者简介:李昊,男,1978年2月生,2010年获中国石油大学(华东)油气井工程专业博士学位,现为中国石油大学(华东)石油工程学院副教授,主要从事油气井流体力学与工程、海洋油气工程领域的研究工作。Email:upc-lihao@upc.edu.cn
基金资助:
国家重点研发计划项目(2024YFC3014003)、高技术船舶科研项目(CBG2N21-4-2-4)和国家自然科学基金项目(No.52288101)资助。

An early kick detection method based on coupled formation-wellbore flow response characteristics

Li Hao^1,2, Lu Jianshe^1,2, Sun Baojiang^1,2, Ren Meipeng³, Wang Chunsheng⁴, Zhang Yaoming⁴, Yin Zhiming³, Li Xiubin⁵

1. State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Shandong Qingdao 266580, China;
2. School of Petroleum Engineering, China University of Petroleum, Shandong Qingdao 266580, China;
3. CNOOC Research Institute Co., Ltd., Beijing 100027, China;
4. Oil and Gas Technology Research Institute, PetroChina Tarim Oilfield Company, Xinjiang Korla 841004, China;
5. Geological Research Institute, CNPC Xibu Drilling Engineering Company Limited, Xinjiang Karamay 834000, China

Received:2025-07-16 Revised:2026-04-28 Published:2026-08-04

摘要/Abstract

摘要： 溢流的早期监测是保障钻井作业安全高效进行的前提条件。针对当前溢流监测研究仅关注地层流体侵入后在井筒内的运移滑脱对井筒流动状态的影响而忽略侵入过程本身对井筒流动状态的影响所导致的监测滞后性问题,通过开展测井、录井参数的溢流响应特征实验研究,揭示了溢流初期地层与井筒耦合流动过程中地层流体侵入对井底压力、立压、泥浆池液面、套压等测、录井多参数的影响机制,发现了地层流体侵入期间井底压力和立压呈现出的"先增后降"响应规律。考虑地层流体侵入对立压、出口流量、泥浆池增量等流动参数的扰动机制,引入扩展Kalman滤波(EKF)预测方法,建立了由地层—井筒耦合流动系统状态方程和观测方程组成的溢流早期监测多输入—多输出系统状态表征模型,提出了基于地层与井筒耦合流动响应特征的溢流早期监测方法。利用实验数据和钻井现场实测数据对该模型的有效性进行了评价,结果表明:该模型能够敏锐捕捉早期压力扰动,并将其转化为对泥浆池增量演变趋势的实时预测。科学实验井实验条件下,提出的溢流早期监测方法的预警时间较泥浆池增量监测方法平均提前24.1 s; 实钻数据条件下,模型溢流发现时间较传统泥浆池增量监测方法提前了21 min,有效解决了钻进期间溢流早期识别难题,为保障钻井安全提供了理论和技术支撑。

关键词: 溢流早期监测, 溢流响应特征, 井筒与地层耦合, 扩展Kalman滤波, 井控安全

Abstract: Early kick detection is a fundamental prerequisite for ensuring the safety and efficiency of drilling operations. Current research on kick monitoring primarily focuses on how the post-intrusion migration and slippage of formation fluids alter wellbore flow dynamics, while neglecting the influence of the intrusion process itself. This oversight contributes significantly to detection lag. Through experimental investigations of the logging and mud-logging parameter responses, this study elucidates the mechanisms by which formation fluid intrusion influences multiple parameters, including bottomhole pressure, standpipe pressure, pit level, and casing pressure, during the coupled formation-wellbore flow process at kick onset. The findings reveal a distinct response pattern of initial increase followed by a subsequent decrease in both bottomhole and standpipe pressures during formation fluid intrusion. By incorporating the perturbation mechanisms of formation fluid intrusion on flow parameters—such as standpipe pressure, outlet flow rate, and pit gain—this study introduces an Extended Kalman Filter (EKF) predictive approach to establish a multi-input-multi-output (MIMO) system state characterization model for early kick detection. This model comprises state and observation equations derived from the coupled formation-wellbore flow system. Based on this framework, an early kick detection methodology centered on coupled flow response characteristics was proposed. The efficacy of the developed model was rigorously evaluated and validated using both experimental datasets and field-measured drilling data. The results demonstrate that the model effectively captures subtle early pressure perturbations and translates them into real-time predictions of the pit gain evolutionary trends. Under scientific experimental well conditions, the proposed early kick detection method achieved a warning lead time averaging 24.1 seconds ahead of conventional pit gain monitoring. When applied to field drilling datasets, the model detected the kick 21 minutes earlier than traditional pit gain methods. This approach effectively addresses early kick identification challenges during drilling operations, providing significant theoretical and technical support for ensuring drilling safety.

Key words: early monitoring of kick, kick response characteristics, wellbore-formation coupling, extended Kalman filter, well control safety

中图分类号:

TE256

李昊, 路建社, 孙宝江, 任美鹏, 王春生, 张耀明, 殷志明, 李秀彬. 基于地层与井筒耦合流动响应特征的溢流早期监测方法[J]. 石油学报, 2026, 47(7): 1518-1530.

Li Hao, Lu Jianshe, Sun Baojiang, Ren Meipeng, Wang Chunsheng, Zhang Yaoming, Yin Zhiming, Li Xiubin. An early kick detection method based on coupled formation-wellbore flow response characteristics[J]. Acta Petrolei Sinica, 2026, 47(7): 1518-1530.

参考文献

[1] 张更,李军,杨宏伟,等. 基于井筒—地层温度分布特征的溢漏同存工况检测方法[J].石油学报,2026,47(4):920-933.
ZHANG Geng,LI Jun,YANG Hongwei,et al.Detection method for the co-existence of well kick and lost circulation based on wellbore and formation temperature distribution characteristics[J].Acta Petrolei Sinica,2026,47(4):920-933.
[2] 朱海燕,王智辉,范宇,等.碳酸盐岩地层孔隙压力预测方法研究进展及展望[J].石油学报,2025,46(8):1628-1646.
ZHU Haiyan,WANG Zhihui,FAN Yu,et al.Research progress and application prospects of prediction methods for pore pressure in carbonate formations[J].Acta Petrolei Sinica,2025,46(8):1628-1646.
[3] 陈平,马天寿.深水钻井溢流早期监测技术研究现状[J].石油学报,2014,35(3):602-612.
CHEN Ping,MA Tianshou.Research status of early monitoring technology for deepwater drilling overflow[J].Acta Petrolei Sinica,2014,35(3):602-612.
[4] SANTOS H M,CATAK E,KINDER J I,et al.First field applications of microflux control show very positive surprises[C]//IADC/SPE Managed Pressure Drilling & Underbalanced Operations.Galveston:IADC/SPE,2007:SPE-108333-MS.
[5] 汪海阁,夏焱,丁吉平,等.中石油深层煤岩气钻完井工程技术现状及展望[J].石油学报,2025,46(8):1576-1588.
WANG Haige,XIA Yan,DING Jiping,et al.Current status and prospect of CNPC’s deep coal-rock gas drilling and completion engineering technologies[J].Acta Petrolei Sinica,2025,46(8):1576-1588.
[6] OBI C E,FALOLA Y,MANIKONDA K,et al.A machine learning approach for gas kick identification[J].SPE Drilling & Completion,2023,38(4):663-681.
[7] SHI Xiaoyan,ZHOU Yingcao,ZHAO Qing,et al.A new method to detect influx and loss during drilling based on machine learning[C]//International Petroleum Technology Conference.Beijing:IPTC,2019:IPTC-19489-MS.
[8] 李玉飞,张博,孙伟峰.基于SVM和D-S证据理论的早期溢流智能识别方法[J].钻采工艺,2020,43(5):27-30.
LI Yufei,ZHANG Bo,SUN Weifeng.Research on intelligent early kick identification method based on SVM and D-S evidence theory[J].Dr illing & Production Technology,2020,43(5):27-30.
[9] ZHAO Yulai,HUANG Zhiqiang,XIN Fubin,et al.Kick prediction method based on artificial neural network model[J].Energies,2022,15(16):5912.
[10] ZHANG Dezhi,SUN Weifeng,DAI Yongshou,et al.Intelligent kick detection using a parameter adaptive neural network[J].Geoenergy Science and Engineering,2024,234:212694.
[11] 刘成路.数据驱动模式下溢流监测及智能压井系统研究与应用[D].西安:西安石油大学,2022.
LIU Chenglu.Research and application of kick monitoring and intelligent well killing system under data-driven mode[D].Xi’an:Xi’an Shiyou University,2022.
[12] 朱志强,廖茂林,李牧,等.基于微型测量器动力学信号分析的井下气侵定量检测方法[J].石油学报,2026,47(3):659-673.
ZHU Zhiqiang,LIAO Maolin,LI Mu,et al.Quantitative detection method of downhole gas kick based on dynamic signal analysis of a micro-measurer[J].Acta Petrolei Sinica,2026,47(3):659-673.
[13] 刘汉桥.基于数据挖掘的海洋钻井井涌早期预测方法研究[D].青岛:中国石油大学(华东),2020.
LIU Hanqiao.Study on early prediction for offshore drilling well kick based on data mining[D].Qingdao:China University of Petroleum,2020.
[14] MUOJEKE S,VENKATESAN R,KHAN F.Supervised data-driven approach to early kick detection during drilling operation[J].Journal of Petroleum Science and Engineering,2020,192:107324.
[15] ZHANG Zheng,LAI Xuzhi,WU Min,et al.Fault diagnosis based on feature clustering of time series data for loss and kick of drilling process[J].Journal of Process Control,2021,102:24-33.
[16] 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.
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.
[17] 徐宝昌,尤香凝,孟卓然,等.基于回压激励响应的实时地层压力反演方法[J].石油学报,2024,45(6):1009-1018.
XU Baochang,YOU Xiangning,MENG Zhuoran,et al.Real-time formation pressure inversion method based on back-pressure excitation response[J].Acta Petrolei Sinica,2024,45(6):1009-1018.
[18] WANG Zizhen,ZHAO Ru,ZANG Yanbin,et al.Downhole detection of gas kick using low-frequency elastic wave:multiphysics modeling and its implications[J].Gas Science and Engineering,2024,125:205297.
[19] CHEN Xin,HE Miao,XU Mingbiao.Early gas kick detection-inversion-control integrated system:the significance of applications of managed pressure drilling:a review[J].Geoenergy Science and Engineering,2023:229:212134.
[20] 徐宝昌,孟卓然,陈贻祺,等.控压钻井井下气侵工况的自适应参数及状态估计[J].控制与决策,2024,39(2):551-558.
XU Baochang,MENG Zhuoran,CHEN Yiqi,et al.Adaptive parameter and state estimation of managed pressure drilling while downhole gas kick[J].Control and Decision,2024,39(2):551-558.
[21] SHI Yadong,HAO Hongda,LIU Rentong,et al.Drilling overflow diagnosis based on the fusion of physical and intelligent algorithms[J].Processes,2025,13(2):577.
[22] WANG Biao,LI Jun,ZHANG Geng,et al.A novel early gas kick monitoring method using the difference between downhole dual measurement points pressure and a genetic algorithm-based model[J].Geoenergy Science and Engineering,2023,231:212371.
[23] JIANG Hailong,LIU Gonghui,LI Jun,et al.A realtime drilling risks monitoring method integrating wellbore hydraulics model and streaming- data-driven model parameter inversion algorithm[J].Journal of Natural Gas Science and Engineering,2021,85:103702.
[24] 孙宝江,周梓强,孙骞.油气钻采工程中的人工智能方法研究进展[J].石油学报,2025,46(11):2141-2173.
SUN Baojiang,ZHOU Ziqiang,SUN Qian.Progress on artificial intelligence methods in oil and gas drilling and production[J].Acta Petrolei Sinica,2025,46(11):2141-2173.
[25] 陈庭根,管志川.钻井工程理论与技术[M].东营:中国石油大学出版社,2006:145-154.
CHEN Tinggen,GUAN Zhichuan.Drilling engineering theory and technology[M].Dongying:China University of Petroleum Press,2006:145-154.
[26] KAASA G O,STAMNES Ø N,IMSLAND L,et al.Simplified hydraulics model used for intelligent estimation of downhole pressure for a managed-pressure-drilling control system[J].SPE Drilling & Completion,2012,27(1):127-138.
[27] SHISHAVAN R A,HUBBELL C,PEREZ H D,et al.Combined rate of penetration and pressure regulation for drilling optimization using high speed telemetry[R].SPE 170275,2014.
[28] 严锦涛,陈砚桥,刘晓威.基于卡尔曼滤波理论的质量数据评估方法研究[J].舰船电子工程,2018,38(8):137-140.
YAN Jintao,CHEN Yanqiao,LIU Xiaowei.Research on quality data evaluation method based on Kalman filter theory[J].Ship Electronic Engineering,2018,38(8):137-140.
[29] 王可东.Kalman滤波基础及MATLAB仿真[M].北京:北京航空航天大学出版社,2019.
WANG Kedong.Kalman filtering basis and MATLAB simulation[M].Beijing:Beijing University of Aeronautics and Astronautics Press,2019.
[30] 张英坤.扩展卡尔曼滤波在非线性系统中的应用[J].中国科技信息,2018(7):71-72.
ZHANG Yingkun.Application of extended Kalman filter in nonlinear systems[J].China Science and Technology Information,2018(7):71-72.
[31] 陆镭.拉普拉斯变换及其应用[J].安庆师范学院学报(自然科学版),2002,8(1):49-50.
LU Lei.Haplace transformation and its application[J].Journal of Anqing Teachers College(Natural Science),2002,8(1):49-50.
[32] 潘定平,朱宇锋.非线性卡尔曼滤波在机载方位测量系统中的应用[J].电子信息对抗技术,2023,38(3):32-38.
PAN Dingping,ZHU Yufeng.Application of nonlinear Kalman filter in airborne azimuth measurement system[J].Electronic Information Warfare Technology,2023,38(3):32-38.

基于地层与井筒耦合流动响应特征的溢流早期监测方法

An early kick detection method based on coupled formation-wellbore flow response characteristics

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