基于BP神经网络的小角度井斜方位角误差补偿研究

井斜角与方位角是井眼轨迹计算中的主要测量参数,然而与常规井斜时方位角误差相比,小角度井斜下测斜仪的方位角测量误差更大。为了提高测斜仪在小角度井斜下的方位角测量精度,基于BP神经网络算法对5°~10°小角度井斜下方位角的测量进行了补偿。文中以标准井斜角和实测方位角构成的二维向量作为输入,以标准方位角构成的一维向量作为输出,建立了双入单出网络模型。采用随机选取的方式将学习样本分为训练集与测试集,使网络具有较好的泛化能力。仿真测试结果表明,该BP神经网络误差校正模型运行稳定,补偿精度达到10^-6,可将小角度井斜下方位角的测量精度从±5.3°提高至±1.7°以内。

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

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°.