基于BP神经网络的小角度井斜方位角误差补偿研究

doi:10.16180/j.cnki.issn1007-7820.2022.05.006

Abstract

Abstract:

Deviation angle and azimuth angle are the main measurement parameters in borehole trajectory calculation. However, the measurement error of azimuth angle of the inclinometer with small-angle deviation is larger than that with conventional deviation. In order to improve the accuracy of azimuth measurement of inclinometer under small-angle deviation, the measurement of azimuth angle under 5°~10° is compensated based on BP neural network algorithm. The neural network is established, whose input is two dimensional vector including standard deviation angle and measured azimuth, and output is the expected azimuth. The learning samples are divided into training sets and test sets by random selection, which can make the network have better generalization ability. The simulation results show that the BP neural network error correction model runs stably, with a compensation accuracy of 10^-6, which can increase the measurement accuracy of the low angle of the small angle well deviation from ±5.3° to within ±1.7°.

Key words: inclinometer accuracy, small-angle deviation, azimuth correction, neural network, BP algorithm, error compensation, correction model, gradient descent

CLC Number:

TN98

DING Huihui,SHAO Tingting,QIAO Xi. Research on Azimuth Error Compensation Based on BP Neural Network at Small-Angle Deviation[J].Electronic Science and Technology, 2022, 35(5): 33-37.

Figures/Tables 5

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Figure 2.

Table 1.

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Figure 4.

References 19

[1]	高爽, 张若愚, 李玲玲. 光纤陀螺随钻测斜仪井眼轨迹校正方法研究[J]. 测井技术, 2020, 44(1):21-26.
	Gao Shuang, Zhang Ruoyu, Li Lingling. Wellbore trajectory correction method for fiber-optic-gyro MWD inclinometers[J]. Well Logging Technology, 2020, 44(1):21-26.
[2]	徐斌, 顾伟. 新型测斜仪测量误差分析[J]. 传感技术学报, 2015, 28(7):1035-1040.
	Xu Bin, Gu Wei. The analysis of the measurement error for the new inclinometer[J]. Chinese Journal of Sensors and Actuators, 2015, 28(7):1035-1040.
[3]	王小龙. 铁磁钻具引起测斜仪方位角误差的校正方法研究[J]. 地球物理学进展, 2019, 34(6):2521-2525.
	Wang Xiaolong. Study on correction method of azimuth error of inclinometer caused by ferromagnetic drilling tool[J]. Progress in Geophysics, 2019, 34(6):2521-2525.
[4]	王成立, Chikhotkin V F, 卢春华. 随钻测斜仪误差补偿实验研究[J]. 煤田地质与勘探, 2016, 44(4):147-152.
	Wang Chengli, Chikhotkin V F, Lu Chunhua. Error compensation of measurement while drilling inclinometer[J]. Coal Geology & Exploration, 2016, 44(4):147-152.
[5]	邵婷婷, 亓宏涛, 雷文礼. 基于BP神经网络的测斜仪方位角误差补偿研究[J]. 信息技术, 2016(10):71-73.
	Shao Tingting, Qi Hongtao, Lei Wenli. Research on error compensation of azimuth based on BP neural network[J]. Information Technology, 2016(10):71-73.
[6]	龙达峰, 刘俊, 张晓明. 陀螺测斜仪小角度井斜角测量的姿态提取方法[J]. 传感技术学报, 2013, 26(6):883-886.
	Long Dafeng, Liu Jun, Zhang Xiaoming. Attitude extraction method of gyro inclinometer at small angles of inclination measurement[J]. Chinese Journal of Sensors and Actuators, 2013, 26(6):883-886.
[7]	徐想. 基于三维传感器的测斜仪安装误差校正分析[J]. 石化技术, 2019, 26(10):283-284.
	Xu Xiang. Error correction analysis of inclinometer installation based on three-dimensional sensor[J]. Petrochemical Industry Technology, 2019, 26(10):283-284.
[8]	雷晓荣. 一种矿井磁阻式测斜仪方位角二次校正算法[J]. 煤矿安全, 2020, 51(6):154-157.
	Lei Xiaorong. A secondary correction algorithm of azimuth for mine magnetoresistive inclinometer[J]. Safety in Coal Mines, 2020, 51(6):154-157.
[9]	乔美英, 王波, 肖学军, 等. 煤矿井下随钻测斜仪误差联合校正方法[J]. 煤田地质与勘探, 2020, 48(2):202-208.
	Qiao Meiying, Wang Bo, Xiao Xuejun, et al. Joint correction method of errors of MWD inclinometer in underground coal mine[J]. Coal Geology & Exploration, 2020, 48(2):202-208.
[10]	李大虎, 李秋科, 王文才, 等. 基于MIV特征选择与PSO-BP神经网络的煤炭发热量预测[J]. 煤炭工程, 2020, 52(11):154-160.
	Li Dahu, Li qiuke, Wang Wencai, et al. Prediction of coal calorific value based on MIV characteristic variable selection and PSO-BP neural network[J]. Coal Engineering, 2020, 52(11):154-160.
[11]	朱卫云, 付东翔, 葛懂林. 基于RBF神经网络的永磁同步伺服电机控制系统[J]. 电子科技, 2016, 29(1):161-164.
	Zhu Weiyun, Fu Dongxiang, Ge Donglin. PMSM control based on RBF neural network[J]. Electronic Science and Technology, 2016, 29(1):161-164.
[12]	杨雨洁, 张美凤, 刘珂琴, 等. RBF-BP复合神经网络在变压器故障诊断中的应用[J]. 信息技术与信息化, 2020(6):136-137.
	Yang Yujie, Zhang Meifeng, Liu Keqin, et al. Application of RBF-BP composite neural network in transformer fault diagnosis[J]. Information Technology and Informatization, 2020(6):136-137.
[13]	管胜, 阮方鸣, 周奎. BP神经网络预测电极速度影响放电参数分析[J]. 电子科技, 2019, 32(6):43-48.
	Guan Sheng, Ruan Fangming, Zhou Kui, et al. Analysis of discharge parameters affected by BP neural network predicting electrode velocity[J]. Electronic Science and Technology, 2019, 32(6):43-48.
[14]	彭彬彬, 闫献国, 杜娟. 基于BP和RBF神经网络的表面质量预测研究[J]. 表面技术, 2020, 49(10):324-328.
	Peng Binbin, Yan Xianguo, Du Juan. Surface quality prediction based on BP and RBF neural networks[J]. Surface Technology, 2020, 49(10):324-328.
[15]	郭卫, 杨鹏飞, 张武刚, 等. 基于BP神经网络模型的MEMS加速度计误差补偿方法[J]. 中国测试, 2018, 44(3):109-113.
	Guo Wei, Yang Pengfei, Zhang Wugang, et al. Error compensation method of MEMS accelerometer based on BP neural network model[J]. China Measurement & Test, 2018, 44(3):109-113.
[16]	Tariq R. Make your own neural network[M]. Charleston: CreateSpace Independent Publishing Platform, 2016.
[17]	胡德敏, 普芳. 一种改进的LeNet网络[J]. 电子科技, 2019, 32(3):53-56.
	Hu Demin, Pu Fang. An improved LeNet network[J]. Electronic Science and Technology, 2019, 32(3):53-56.
[18]	李雪菁. 基于数据挖掘技术的电子商务客户关系管理[J]. 计算机与数字工程, 2018, 46(9):1834-1838.
	Li Xuejing. Research on customer relationship management representation based on data mining[J]. Computer & Digital Engineering, 2018, 46(9):1834-1838.
[19]	邵婷婷, 张博超, 周美丽, 等. 基于RBF神经网络的测斜仪方位角校正研究[J]. 国外电子测量技术, 2016, 35(2):77-79.
	Shao Tingting, Zhang Bochao, Zhou Meili, et al. Research on error compensation of azimuth based on RBF neural network[J]. Foreign Electronic Measurement Technology, 2016, 35(2):77-79.

井斜角	实测方位角	理论方位角	误差
5.0°	268.5°	270°	1.5°
5.4°	331.7°	330°	1.7°
5.8°	61.7°	60°	1.7°
6.0°	298.3°	300°	1.7°
6.3°	58.3°	60°	1.7°
6.7°	275.5°	270°	5.3°
7.0°	358.2°	360°	1.8°
7.4°	358.2°	360°	1.8°
7.8°	298.2°	300°	1.8°
8.0°	181.3°	180°	1.3°
8.3°	211.8°	210°	1.8°
8.7°	358.2°	360°	1.8°
9.0°	61.7°	60°	1.7°
9.3°	358.7°	360°	1.3°
9.8°	58.2°	60°	1.8°
10.0°	358.3°	360°	1.7°

Research on Azimuth Error Compensation Based on BP Neural Network at Small-Angle Deviation

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