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探管式智能钻头参数测量装置研制与现场试验

黄哲

黄哲. 探管式智能钻头参数测量装置研制与现场试验[J]. 石油钻探技术,2024,52(4):34-43. DOI: 10.11911/syztjs.2024004
引用本文: 黄哲. 探管式智能钻头参数测量装置研制与现场试验[J]. 石油钻探技术,2024,52(4):34-43. DOI: 10.11911/syztjs.2024004
HUANG Zhe. Development and field test of probe-type intelligent bit parameter measurement device [J]. Petroleum Drilling Techniques, 2024, 52(4):34-43. DOI: 10.11911/syztjs.2024004
Citation: HUANG Zhe. Development and field test of probe-type intelligent bit parameter measurement device [J]. Petroleum Drilling Techniques, 2024, 52(4):34-43. DOI: 10.11911/syztjs.2024004

探管式智能钻头参数测量装置研制与现场试验

基金项目: 中国石化优秀青年创新基金项目“智能钻头参数感知与优化控制技术”(编号:P23219)、中石化石油工程技术服务有限公司课题“PDC钻头四参数采集系统研制与应用”(编号:AMBG220089)和山东省中央引导地方科技发展专项资金项目“海洋油气钻井数字化技术创新平台建设”(编号:YDZX2022015)联合资助。
详细信息
    作者简介:

    黄哲(1990—),男,山东东营人,2013年毕业于中国石油大学(北京)石油工程专业,2020年获中国石油大学(北京)油气井工程专业博士学位,高级工程师,主要从事微小井眼轨迹测控、井下装备研制、优化参数钻井、智能钻井、软件开发等方面的研究工作。E-mail:bob202303@163.com

  • 中图分类号: TE921+.1

Development and Field Test of Probe-Type Intelligent Bit Parameter Measurement Device

  • 摘要:

    现有螺杆上游的测量装置无法获得钻头位置处的真实数据,智能钻井在决策控制层面存在严重的数据偏见与不可预测的实施风险。为此,研制了一种探管式智能钻头参数测量装置,可以安装在钻头内部,在不改变现有钻具组合与施工工艺的前提下直接采集钻头处的真实数据;硬件结构仿真优化结果表明,30 L/s排量与80 MPa环空压力下,理论水力压耗小于0.1 MPa、安全系数为3.06。同时,基于六面法与ARMA+移动Kalman滤波,实现了该装置的误差标定与降噪。该装置在胜利工区现场试验9井次,配套井下工程参数测量短节进行了装置功能验证与井下数据对比,融合综合录井数据进行了试验井提速分析与参数优化决策。试验结果表明,探管式智能钻头参数测量装置能够获取钻头位置处的真实数据,能够为针对性提速提效分析与钻井参数优化提供可靠依据。

    Abstract:

    The existing measuring device installed on the upside of current PDM motor fails to get the real data at the bit position, and the intelligent drilling suffers from serious data biases and unpredictable implementation risks at decision-making and control level. Therefore, a probe-type intelligent bit parameter measurement device was developed, which could be installed inside the bit and collect the real data of the bit without any change on the existing bottom hole assembly (BHA) and operating process. The hardware structure simulation optimization of the device was performed, and the theoretical hydraulic pressure loss was less than 0.1 MPa with a safety factor of 3.06 under the displacement of 30 L/s and annular pressure of 80 MPa. The error calibration and noise reduction of this device were achieved by six-sided method measurement together with auto regressive moving average (ARMA) model and moving Kalman filter. Field trials of the device have been conducted in 9 wells in Shengli Oilfield. The function of the device was verified, and the downhole data was compared based on the downhole engineering parameter measurement sub. The speed-up analysis and parameter optimization of the pilot wells were carried out through integrating the comprehensive logging data. The test results show that the probe-type intelligent bit parameter measurement device can collect the real data of the bit position, which can provide a reliable basis for the speed-up and efficiency improvement analysis and drilling parameter optimization.

  • 图  1   探管式智能钻头参数测量装置结构与装配示意

    Figure  1.   Structure and assembly of probe-type intelligent bit parameter measurement device

    图  2   探管式智能钻头参数测量装置流场分布云图

    Figure  2.   Flow field distribution nephogram of probe-type intelligent bit parameter measurement device

    图  3   各结构下累计冲蚀量的对比

    Figure  3.   Comparison of cumulative erosion volume under different structures

    图  4   探管式智能钻头参数测量装置循环压耗图版

    Figure  4.   Circulating pressure loss chart of probe-type intelligent bit parameter measurement device

    图  5   支撑臂在轴扭作用下的应力集中

    Figure  5.   Stress concentration of supporting arm under shaft torque

    图  6   探管式智能钻头参数测量装置应力分布云图(剖面)

    Figure  6.   Stress distribution nephogram of probe-type intelligent bit parameter measurement device (section view)

    图  7   Allen方差曲线(原始数据)

    Figure  7.   Allen variance curve (original data)

    图  8   随机噪声ARMA建模

    Figure  8.   ARMA modeling of random noise

    图  9   随机噪声降噪效果对比

    Figure  9.   Comparison of random noise reduction effects

    图  10   智能钻头与工程参数测量短节采集数据特征对比

    Figure  10.   Comparison of data characteristics collected by intelligent drill bit and engineering parameter measurement sub

    图  11   胜利油田义页某井智能钻头采样所得原始数据

    Figure  11.   Original data obtained by intelligent bit sampling in a well of Yiye, Shengli Oilfield

    图  12   胜利油田义页某井取心钻进段测量数据与工况诊断

    Figure  12.   Measurement data and operating condition diagnosis of core drilling section in a well of Yiye, Shengli Oilfield

    图  13   胜利油田义页某井取心钻进段频谱分析

    (a)侧向振动X;(b)侧向振动Z;(c)轴向振动Y;(d)侧向振动X2+Y2

    Figure  13.   Spectrum analysis of core drilling section in a well of Yiye, Shengli Oilfield

    图  14   测量频谱分析(部分)

    Figure  14.   Spectral analysis (part)

    图  15   测量数据模态分解

    Figure  15.   Modal decomposition of measured data

    图  16   目标井深关键钻井参数预测云图(部分)

    Figure  16.   Predict nephogram of key parameters in target well depth (partial)

    图  17   目标井深钻井参数多目标博弈优化云图

    Figure  17.   Optimized nephogram of drilling parameters by multi-objective game in target well depth

    表  1   不同端部结构探管式装置的循环压耗

    Table  1   Circulating pressure loss of probe tube devices with different end structures

    端部结构尺寸 循环压耗/ MPa
    外形 外径/ mm 两端结构优化后 上游结构优化后
    半球形 D1= D2=24 0.0410 0.1020
    半球形 D1= D2=26 0.0540 0.1270
    半球形 D1= D2=28 0.0740 0.1560
    半球形 D1= D2=30 0.0970 0.1910
    椭球形 D1=24,D2=36 0.0350 0.0901
    椭球形 D1=24,D2=48 0.0310 0.0904
     注:D1为椭球小径或半球直径(与探管直径相等),mm;D2为椭球大径或半球直径,mm。
    下载: 导出CSV

    表  2   探管式智能钻头参数测量装置循环压耗对照(30 L/s)

    Table  2   Circulating pressure loss of probe-type intelligent bit parameter measurement device (30 L/s)

    钻头直径/mm 连接螺纹 内径/mm 循环压耗/MPa
    142.9~171.5 3½" REG 38 0.635 550
    190.5~212.7 4½" REG 58 0.019 350
    241.3~342.9 6⅝" REG 89 0.007 848
    下载: 导出CSV

    表  3   取心钻进段振动特征与作业参数的关系

    Table  3   Relationship between vibration characteristics of core drilling section and operation parameters

    岩心长度/m 钻压/kN 轴向振动加速度/g 侧向振动加速度/g
    0~6.0 60~70 0.7 (低) 9.0 (严重)
    6.0~10.0 55~60 1.2 (中) 4.5 (高)
    10.0~13.0 40~45 1.2 (中) 5.0 (高)
    13.0~13.7 55 0.9 (低) 9.0 (严重)
    14.3~15.3 35~95 0.7(低) 8.5(严重)
    15.3~16.0 85~95 0.6 (低) 7.0 (严重)
    下载: 导出CSV
  • [1]

    Baker Hughes. MultiSense dynamics mapping system[EB/OL]. [2023-01-20].https://www.bakerhughes.com/drilling/drilling-optimization-services/multisense-dynamics-mapping-system.

    [2]

    Halliburton. Cerebro® in-bit sensing[EB/OL]. [2023-01-20].https://www.halliburton.com/en/products/cerebro-bit-sensor-package.

    [3]

    NOV. BlackBox eclipse II tool[EB/OL]. [2023-01-20].https://www.nov.com/products/blackbox-eclipse-ii-tool.

    [4]

    LEDGERWOOD III L W, JAIN J R, HOFFMANN O J, et al. Downhole measurement and monitoring lead to an enhanced understanding of drilling vibrations and polycrystalline diamond compact bit damage[J]. SPE Drilling & Completion, 2013, 28(3): 254–262.

    [5]

    CORNEL S, VAZQUEZ G. Use of big data and machine learning to optimise operational performance and drill bit design[R]. SPE 202243, 2020.

    [6]

    PELFRENE G, CUILIER B, EZZEDDINE D, et al. Setting a new standard: PDC bits equipped with compact vibration recorders monitor entire run and reveal stick-slip mitigation system dysfunction and downhole motor under performance[R]. SPE 204112, 2021.

    [7]

    TOWNSEND T, MOSS W, HEINISCH D, et al. Advanced high frequency in-bit vibration measurement including independent, spatially separated sensors for proper resolution of vibration components including lateral, radial, and tangential acceleration[R]. SPE 208110, 2021.

    [8]

    XIAO Y, QIU H, BUTT S D. Measurement and analysis of drill-bit motions: a case study for passive-vibration assisted rotary drilling (pVARD) tool[R]. ARMA-2020-2036, 2020.

    [9]

    SUGIURA J, JONES S. A drill bit and a drilling motor with embedded high-frequency (1600 Hz) drilling dynamics sensors provide new insights into challenging downhole drilling conditions[J]. SPE Drilling & Completion, 2019, 34(3): 223–247.

    [10]

    CHEN Shilin, WISINGER J, DUNBAR B, et al. Identification and mitigation of friction- and cutting-action-induced stick/slip vibrations with PDC bits[J]. SPE Drilling & Completion, 2020, 35(4): 576–587.

    [11] 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.

    LI Gensheng, SONG Xianzhi, TIAN Shouceng. Intelligent drilling technology research status and development trends[J]. Petroleum Drilling Techniques, 2020, 48(1): 1–8.

    [12] 黄哲. 面向油气钻井振动测量的空间三轴加速度传感器阵列研究[J]. 电子测量技术,2021,44(8):155–160.

    HUANG Zhe. Method of drilling vibration measurement based on spatial array of accelerometers[J]. Electronic Measurement Technology, 2021, 44(8): 155–160.

    [13]

    HUANG Zhe, YAN Xiuliang, DAI Yanan. Vibration control in optimized drilling and key issues to be applied in new clean geo-energy exploitation[C]//Proceedings of the 3rd International Conference on Green Energy, Environment and Sustainable Development (GEESD2022). Amsterdam: IOS Press, 2022: 38-48.

    [14]

    CHEN Xiwu, HUANG Zhe. Novel tool of in-bit measurement for new clean geo-energy exploitation[C]//Proceedings of the 3rd International Conference on Green Energy, Environment and Sustainable Development (GEESD2022). Amsterdam: IOS Press, 2022: 1223-1232.

    [15] 黄哲,吴仲华,李成,等. 智能钻头技术研究与应用探索[J]. 石油机械,2023,51(10):67–76.

    HUANG Zhe, WU Zhonghua, LI Cheng, et al. Research and application of intelligent bit technology[J]. China Petroleum Machinery, 2023, 51(10): 67–76.

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出版历程
  • 收稿日期:  2023-07-03
  • 修回日期:  2024-04-22
  • 网络出版日期:  2024-05-28
  • 刊出日期:  2024-08-25

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