@inproceedings{12f2389616b3475da4b2d3f75565ec38,
title = "Well log data inversion using radial basis function network",
abstract = "We adopt the radial basis function network (RBF) for well log data inversion. We propose the 3 layers RBF. Inside RBF, the first layer is the K-means clustering method and PFS-test. Then the 1-layer perceptron is replaced by 2-layer perceptron. It can do more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at 2-layer perceptron. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). 25 simulated well log data are used in the training. From experimental results, the network with 10 input data, first layer with 27 nodes, second layer with 9 hidden nodes and 10 output nodes can get the smallest average mean absolute error in the training. After training in the network, we apply it to do the inversion of the 6 simulated well log data and 1 real field well log data to get the inverted Ct. Result is good. It shows that the RBF can do the well log data inversion.",
keywords = "perceptron, input feature, well logging, node, log analysis, radial basis function network, inversion, 2-layer perceptron, artificial intelligence",
author = "Kou-Yuan Huang and Shen, {Liang Chi} and Weng, {Li Sheng}",
year = "2011",
month = sep,
doi = "10.1190/1.3628131",
language = "English",
isbn = "9781618391841",
series = "Society of Exploration Geophysicists International Exposition and 81st Annual Meeting 2011, SEG 2011",
publisher = "Society of Exploration Geophysicists",
pages = "499--503",
booktitle = "Society of Exploration Geophysicists International Exposition and 81st Annual Meeting 2011, SEG 2011",
address = "United States",
note = "Society of Exploration Geophysicists International Exposition and 81st Annual Meeting 2011, SEG 2011 ; Conference date: 18-09-2011 Through 23-09-2011",
}