Higher order neural networks for well log data inversion

Kou-Yuan Huang*, Liang Chi Shen, Chun Yu Chen

*Corresponding author for this work

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back propagation learning rule. From our experimental results, we find the expanding input features can get fast convergence in training and decrease the mean absolute error between the desired output and the actual output. The multilayer perceptron network with 10 input features, the expanding input features to the third order, 8 hidden nodes, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. And then the system is applied on the real well log data.

原文English
主出版物標題2008 International Joint Conference on Neural Networks, IJCNN 2008
頁面2545-2550
頁數6
DOIs
出版狀態Published - 24 十一月 2008
事件2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
持續時間: 1 六月 20088 六月 2008

出版系列

名字Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
國家China
城市Hong Kong
期間1/06/088/06/08

指紋 深入研究「Higher order neural networks for well log data inversion」主題。共同形成了獨特的指紋。

引用此