標題: WELL LOG DATA INVERSION USING RADIAL BASIS FUNCTION NETWORK
作者: Huang, Kou-Yuan
Shen, Liang-Chi
Weng, Li-Sheng
資訊工程學系
Department of Computer Science
關鍵字: Radial basis function network;well log inversion;multilayer perceptron
公開日期: 2011
摘要: We use the radial basis function network (RBF) for well log data inversion. The first step of the network is the K-means clustering. For the second step, we adopt the 2-layer perceptron instead of conventional 1-layer perceptron. The 2-layer perceptron can do the more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at the second step. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). The original features are the network input for training process. According to our experimental results, the three-layer radial basis function can get smaller error between the desired output and the actual output. The network with 10 input features, first layer with 27 nodes, second layer with 10 hidden node, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. After simulation, we apply the network to the real field data. The result is good. It shows that the RBF can do the well log data inversion.
URI: http://hdl.handle.net/11536/15461
ISBN: 978-1-4577-1005-6
期刊: 2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
起始頁: 4439
結束頁: 4442
Appears in Collections:Conferences Paper