关键词: 测斜仪精度, 小井斜, 方位角校正, 神经网络, BP算法, 误差补偿, 校正模型, 梯度下降

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