Reservoir rock permeability prediction using SVR based on radial basis function kernel
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Permeability is one of the most important parameters of the rock reservoir. This parameter represents the ability of fluid pass through the porous medium without changing the structure of the rock which plays a main role in the production rate of a hydrocarbon reservoir. Permeability could be obtained through laboratory using core plugs, although the measurements is highly accurate, but these analyses are costly, time-consuming, and also the core data are not available for all wells. Therefore, permeability is usually predicted using well logs by regression techniques. In this study, support vector regression (SVR) based on radial basis function is developed to estimate permeability in South Pars gas field of Iran. For this purpose, first four electrofacies were identified using a method called multi-resolution graph-based clustering (MRGC), and thereafter the method applied for each facies. To evaluate the prediction model, the correlation coefficient between the real permeability (determined from core plugs) and the estimated was calculated for each electrofacies. The values for the four electrofacies designated obtained 88.2, 78.51, 84.73, and 77.54 percent, respectively. The high amount of obtained correlations for each electrofacies demonstrates the power of the regression model for a reliable permeability prediction of the reservoir.
KeywordsPermeability Electrofacies Support vector regression Radial basis function Well log Core data
Many thanks go to Institute of geophysics university of Tehran for supporting this research and also Mr. Mohammad Reza Ebadi for helping us to provide the appropriate data.
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