基于神经网络的坑道近水平定向孔轨迹预测研究

Study of the predicting model for directional drilling trajectory controlling based on neural network in tunnel

摘要:

当前近水平定向钻孔轨迹测量位置滞后于钻头位置，无法实时获取滞后区钻孔轨迹实际参数，施工过程中控制轨迹时需要人工预测该部分轨迹并为下一步轨迹调整提供依据。为了降低人为因素影响，提高轨迹预测的准确性，基于BP神经网络建立了用于煤矿井下近水平定向孔轨迹控制的孔底空间参数预测模型。选取随钻测量仪器位置及其之前12 m范围的倾角、方位角等13个钻孔空间和轨迹控制参数，经过变换后作为输入参数，构建了一个具有11个输入参数和2个输出参数的4层BP神经网络预测模型，该模型以不同矿区的6个钻孔502组数据为训练样本，得到了网络预测模型参数，并将12组测试数据的预测结果与24名从业技术人员的经验预测结果进行了对比分析。研究结果表明：采用logsig激活函数和（9×6）节点的双隐含层BP神经网络模型，对孔底空间参数（倾角、方位角）的预测绝对误差平均值分别达到0.51°和0.68°，且预测误差服从正态分布，预测结果绝对误差平均值较从业5年以上的技术人员低了35%，现场应用效果较好，满足煤矿井下定向钻进轨迹控制的需要，并为定向钻轨迹智能控制提供了理论与实践基础。

关键词:BP神经网络;坑道钻探;近水平定向孔;轨迹预测;预测模型

Abstract:

In present， the measured location of the near-horizontal directional drlling trajectory is lag behind the bit， and the actural parameters of the delayed area can not be obtained in time， thus artificial prediction should be made for the next trajectory adjustion. In order to decreased the human facts and improve the accuracy of the prediction， a forecasting model is established based on BP neural network which is used for controlling underground directional drilling trajectory in tunnel. The model is a four-layer BP neural network， and it chooses 11 input parameters and 2 output parameters which are changed from 13 borehole space and trajectory controlling parameters from 12m before MWD including dip angles and azimuths etc. The parameters of the net forecasting model is obtained using 502 groups of training data from 6 boreholes in different mining areas. Then the forecasting results of the 12 groups of test data are compared with that of the artificial experience from 24 technicians. The results show that the mean absolute error of the downhole space parameters i.e. dip angle and azimuth are only 0.51° and 0.68° predicted inrespectively by the logsig activation function and the double-hidden-layer BP neural network which has the point structure of 9×6， and the prediction error obeys normal distribution. The accuracy prediction results derived from the BP neural network model is 35% lower than that from the technicians who work more than 5 years， and the effect from the field application is satisfied which meets the needs of drilling trajectory control. The research offers theoratical and practical base for the intelligent directional drilling work.

Key words:BP neural network;underground drilling;near-horizontal borehole;trajectory prediction;forecasting model