Higher order neural networks for well log data inversion

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages2545-2550
Number of pages6
DOIs
StatePublished - 24 Nov 2008
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 1 Jun 20088 Jun 2008

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period1/06/088/06/08

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