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Binary Classification for Hydraulic Fracturing Operations in Oil & Gas Wells via Tree Based Logistic RBF Networks

Journal Name:

  • European Journal of Pure and Applied Mathematics

Publication Year:

  • 2013

Keywords (Original Language):

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Abstract (Original Language): 
In this paper we develop a novel tree based radial basis function neural networks (RBF-NNs) model incorporating logistic regression. We aim to improve the classification performance of logistic regression method by pre-processing the input data in RBF-NN frame. Although the scope of our proposed method is binary classification in this paper, it is easy to generalize it for multi-class classification problems. Furthermore, our model is very convenient to adapt for n < p classification problem that is very popular yet difficult topic in statistics. We show the generalization and classification performance of our model using simulated data. We have also applied our model on a real life data set gathered from hydraulic fracturing in Oil & Gas wells. The results show the high classification performance of our model that is superior to logistic regression. We have coded our model on R software. Logistic Regression applications were carried out using IBM SPSS Version 20.
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REFERENCES

References: 

[1] H. Akaike. Information theory and an extension of the maximum likelihood principle.
In B.N. Petrox and F. Csaki, editors, Second International Symposium on Information
Theory., pages 267–281, Budapest, 1973. Academiai Kiado.
[2] O. Akbilgic and H. Bozdogan. Predictive Subset Selection Using Regression Trees and
REFERENCES 386
RBF Neural Networks Hybridized with the Genetic Algorithm. European Journal of Pure
and Applied Mathematics, 4:467–485, 2011.
[3] O. Akbilgic, H. Bozdogan, and M.E. Balaban. A novel Hybrid RBF Neural Networks
model as a forecaster. Statistics and Computing, 2013.
[4] L. Breiman, J. Freidman, J. C. Stone, and R. A. Olsen. Classification and Regression Trees.
Chapman & Hall, 1984.
[5] J. Caers. Petroleum Geostatistics. Society of Petroleum Engineers, 2005.
[6] D.M. Carson. Our Petroleum Challenge: Sustainability into 21st Century. Canadian Centre
for Energy Information, 7th edition, 2009.
[7] S Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall„ New Jersey,
1999.
[8] Kurt Hornik. The R FAQ, 2013.
[9] R.J. Howlett and L.C. Jain. Radial Basis Function Networks 1: Recent Developments in
Theory and Applications. Physica Verlag, New York, 2001.
[10] J.R. Jones and L.K. Britt. Design and Appraisal of Hydraulic Fractures. Society of
Petroleum Engineers, 2009.
[11] M. Kubat. Decision trees can initialize radial basis function networks. Transactions on
Neural Networks, 9:813–821, 1998.
[12] M. Orr. Combining Regression Trees and RBFs. International Journal of Neural Systems,
10:453–465, 2000.
[13] T. Poggio and F. Girosi. Regularization algorithms for learning that are equivalent to
multilayer networks. Science, New-Series, 247:978–982, 1990.
[14] I.H. Witten, E. Frank, and M.A. Hall. Data Mining: Practical Machine Learning Tools and
Techniques. Morgan Kaufman, 3rd Edition, 2011.
[15] C.H. Yew. Mechanics of Hydraulic Fracturing. Gulf Professional Publishing, 1st edition,
1997.

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