摘要
介绍了陕北石油基地常用磁性电子测斜仪的结构和测斜原理,分析了其本身和工作过程中可能存在的误差及其来源。对井眼姿态测量中主要测量参数之一的方位角,利用前馈神经网络算法,建立了以实测井斜角和方位角构成的二维向量为输入、标准方位角构成的一维向量为输出的三层误差反向传播网络(BP)模型,并用测斜仪的测量数据进行了测试。测试结果表明,采用该BP神经网络补偿算法,可将方位角的实际测量精度从±2.1°提高至±1.7°以内,误差补偿效果较好。
The structure and measurement principle of common magnetic dip meter in North Shaanxi oil base are introduced,whose possible error and its source are analyzed. For the azimuth,one of the main parameters of borehole attitude measurement,according to the feed-forward neural network,a three layers error back propagation( BP) network is established,which input is a two dimensional vector which is made of measured deviation angle and azimuth,and output is the expected azimuth,and the sampling data of the magnetic dip meter are used to test. The experiment results show that,using the BP neural network compensate algorithm,the azimuth precision can be improved from ± 2. 1°to ± 1. 7°or better.
出处
《信息技术》
2016年第10期71-73,共3页
Information Technology
基金
延安市科学技术研究发展计划项目(2014KG-02)
陕西省科技厅项目(2014JM8357)
陕西省教育厅项目(15JK1827)
陕西省2015年省级大学生创新训练基金项目(1433)
延安大学高水平大学学科建设项目(2015SXTS02)
