Permeability Prediction Using Artificial Neural Networks. A Comparative Study Between Back Propagation and Levenberg–Marquardt Learning Algorithms-Reference-Cited by-同舟云学术

Permeability Prediction Using Artificial Neural Networks. A Comparative Study Between Back Propagation and Levenberg–Marquardt Learning Algorithms

Published:2013-10-08 Issue: Volume: Page:653-657
ISSN:2193-8571
Container-title:Lecture Notes in Earth System Sciences
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Author:

Aliouane Leila,Ouadfeul Sid-Ali,Djarfour Noureddine,Boudella Amar

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-32408-6_142

Reference7 articles.

1. Aliouane, L., Ouadfeul, S., Djarfour, N., & Boudella, A. (2012). Petrophysical parameters estimation from well-logs data using multilayer perceptron and radial basis function neural networks. In T. Huang et al. (Eds.), ICONIP 2012, Part V. LNCS 7667 (pp. 730–736). Springer-Verlag.

2. Aqil, M., Kita, I., Yano, A., & Nishiyama, S. (2007). Neural networks for real time catchment flow modeling and prediction. Water Resource Manage, 21, 1781–1796.

3. Balan, B., Mohaghegh, S., & Ameri, S. (1995). State-of-the-art in permeability determination from well log data: Part 1—A comparative study, model development. SPE. 30978. West Virginia: Eastern Regional Conference & Exhibition.

4. Demuth, H., & Beale, M. (1998). Neural network toolbox for use with MATLAB, Users Guide, Version 3. Massachusetts: The MathWorks, Inc.

5. Ellis, D. V., & Singer, J. M. (2007). Well logging for earth scientists (2nd ed.). Dordrecht: Springer.

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