Comparison between neuro-fuzzy and fractal models for permeability prediction
- 117 Downloads
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.
KeywordsPorosity Permeability Neuro-fuzzy Fractal theory Prediction Linear regretion Empirical General Pape equation
Unable to display preview. Download preview PDF.
- 3.Balan, B., Mohaghegh, S., Ameri, S.: State-of-the-art in permeability determination from well log data, part 1: a comparative study, model development. In: Proceedings, SPE Eastern Regional Conference and Exhibition. SPE30978, pp. 1–10, Morgantown, 19–21 September 1995Google Scholar
- 4.Nelson, P.H.: Permeability–porosity relationships in sedimentary rocks. Log Anal. 35(3), 38–62 (1994)Google Scholar
- 5.Shenhav, H.: Lower cretaceous sandstone reservoirs, Israel: petrography, porosity, permeability. AAPG Bull. 55, 2194–2224 (1971)Google Scholar
- 6.Dandekar, A.Y.: Petroleum Reservoir Rock and Fluid Properties, vol. 488. CRC, Taylor & Francis, London (2006)Google Scholar
- 9.Wong, K.W., Wong, P.M., Gedeon, T.D., Fung, C.C.: A state-of-art review of fuzzy logic for reservoir evaluation. APPEA J. 43, 587–593 (2003)Google Scholar