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Comparison between neuro-fuzzy and fractal models for permeability prediction
Computational Geosciences
Springer Netherlands
ISSN 1420-0597 (Print) 1573-1499 (Online)
Volume 13, Number 2 / §Є§р§Я§о 2009 §Ф.
DOI 10.1007/s10596-008-9095-9
pp 181-186
Comparison between neuro-fuzzy and fractal models for permeability prediction
Nuri Hurtado1, 2 , Milagrosa Aldana3 and Julio Torres4

(1) Laboratorio de FЁЄsica TeЁ®rica de SЁ®lidos, CEFITEC, Escuela de FЁЄsica, Universidad Central de Venezuela, Paseo Los Ilustres, Caracas, 1040, Venezuela
(2) Instituto de Nanociencia de AragЁ®n (INA), Universidad de Zaragoza, Zaragoza, Spain
(3) Dpto. de Ciencias de la Tierra, Universidad SimЁ®n BolЁЄvar (USB), Caracas, 1080, Venezuela
(4) Dpto. de Ciencias BЁўsicas, UNEXPO, Antonio JosЁ¦ de Sucre La Yaguara, Caracas, 1020, Venezuela

Received: 19 November 2007 Accepted: 19 June 2008 Published online: 27 July 2008

Abstract We have used different techniques for permeability prediction using porosity core data from one well at the Maracaibo Lake, Venezuela. One of these techniques is statistical and uses neuro-fuzzy concepts. Another has been developed by Pape et al. (Geophysics 64(5):1447ЁC1460, 1999), based on fractal theory and the KozenyЁCCarman equations. We have also calculated permeability values using the empirical model obtained in 1949 by Tixier and a simple linear regression between the logarithms of permeability and porosity. We have used 100% of the permeabilityЁCporosity data to obtain the predictor equations in each case. The best fit, in terms of the root mean-square error, was obtained with the statistical approach. The results obtained from the fractal model, the Tixier equation or the linear approach do not improve the neuro-fuzzy results. We have also randomly taken 25% of the porosity data to obtain the predictor equations. The increase of the input data density for the neuro-fuzzy approach improves the results, as is expected for a statistical analysis. On the contrary, for the physical model based on the fractal theory, the decrease in the data density could allow reaching the ideal theoretical KozenyЁCCarman model, on which are based the fractal equations, and hence, the permeability prediction using these expressions is improved.
Keywords Porosity - Permeability - Neuro-fuzzy - Fractal theory - Prediction - Linear regretion - Empirical - General Pape equation


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Nuri Hurtado
Email: nhurtado@fisica.ciens.ucv.ve



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