Estimation of water saturation from petrophysical logs using radial basis function neural network

Estimation of reservoir water saturation (S_w) is one of the main tasks in well logging. Many empirical equations are available, which are, more or less, based on Archie equation. The present study is an application of Radial Basis Function Neural Network (RBFNN) modeling for estimation of water saturation responses in a carbonate reservoir. Four conventional petrophysical logs (PLs) including DT, LLd, RHOB and NPHI related to four wells of an oil field located in southwest of Iran are taken as inputs and S_w measured from core analysis as output parameter of the model. To compare performance of the proposed model with empirical equations, the same database was applied. Superiority of the RBFNN model over empirical equations was examined by calculating coefficient of determination and estimated root mean squared error (RMSE) for predicted and measured S_w. For the RBFNN model, R² and RMSE are equal to 0.90 and 0.031, respectively, whereas for the best empirical equation, they are 0.81 and 0.042, respectively.