Comparison between neuro-fuzzy and fractal models for permeability prediction |
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| Authors: | Nuri Hurtado Milagrosa Aldana Julio Torres |
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| Institution: | (1) Laboratorio de Física Teórica de Sólidos, CEFITEC, Escuela de Física, Universidad Central de Venezuela, Paseo Los Ilustres, Caracas, 1040, Venezuela;(2) Instituto de Nanociencia de Aragón (INA), Universidad de Zaragoza, Zaragoza, Spain;(3) Dpto. de Ciencias de la Tierra, Universidad Simón Bolívar (USB), Caracas, 1080, Venezuela;(4) Dpto. de Ciencias Básicas, UNEXPO, Antonio José de Sucre La Yaguara, Caracas, 1020, Venezuela |
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| 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):1447–1460, 1999), based on fractal theory and the Kozeny–Carman 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 permeability–porosity 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 Kozeny–Carman model, on which are based the fractal equations,
and hence, the permeability prediction using these expressions is improved. |
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| Keywords: | Porosity Permeability Neuro-fuzzy Fractal theory Prediction Linear regretion Empirical General Pape equation |
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