Well log data inversion using higher order neural networks

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We use the multilayer perceptron for well log data inversion. The gradient descent method is used in the back propagation learning rule. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). The original and the higher order features are used for the training process. According to our experimental results, the expanding higher order input features can get a fast training and a smaller error between the desired output and the actual output. The network with 10 input nodes and expanding the input features to third order, 8 hidden nodes, 10 output nodes, can get the smallest average mean absolute error on simulated well log data. Then, we apply the network to the real field data.

Original languageEnglish
Title of host publication2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Edition1
DOIs
StatePublished - 1 Dec 2008
Event2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States
Duration: 6 Jul 200811 Jul 2008

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Number1
Volume3

Conference

Conference2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
CountryUnited States
CityBoston, MA
Period6/07/0811/07/08

Keywords

  • Higher order
  • Multilayer perceptron
  • Well log inversion

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    Huang, K-Y., Shen, L. C., & Chen, C. Y. (2008). Well log data inversion using higher order neural networks. In 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings (1 ed.). [4779548] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 3, No. 1). https://doi.org/10.1109/IGARSS.2008.4779548