阵列侧向电法测井的快速反演

Fast inversion for array lateral electric-logging

在介绍阵列侧向电极系工作原理及快速正演计算的基础上,通过计算分析了Marquardt法线性反演过于依赖初值及迭代反演速度慢的不足。为避免迭代反演的缺陷,研究了采用BP神经网络进行测井快速反演的方法。通过对测井响应进行井眼校正等预处理,可有效减少网络训练所需样本数,提高反演效率。通过对比不同网络结构的计算精度与计算效率,选取了22个隐含层节点的3层网络,反演结果与真值吻合较好。该反演方法不需给定初值,不需迭代计算,反演100组参数用一般计算机仅耗时3s。

The shortcomings of the linearization Marquardt method were analyzed based on the principles of the array lateral sonde and fast forward calculations. The shortcomings include the need for initial values and the time-consuming computations. A fast inversion method using a BP neural network was developed to avoid the problems of the iterative inversion. The number of training samplings can be effectively reduced by preprocessing with a borehole correction, which enhances the inversion efficiency. A three-layer network with 22 hidden nodes was chosen to compare the accuracy and efficiency for different networks. The inversion results are in good agreement with the actual values and the BP neural network does not need any initial values or iterative calculations. This method generally needs only 3 s to invert 100 groups of parameters.