- - نويسنده Department of Geophysics, College of Basic Science, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran Neyzan Hosseini Marzieh , - - نويسنده Department of Petroleum Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran Kazemzadeh Ezatallah , - - نويسنده Department of Geophysics, College of Basic Science, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran Sobhani Ehsan , - - نويسنده Department of Petrophysics, Iranian Offshore Oil Company, Tehran, Iran Arbab Bita
Water saturation determination is one of the most important tasks in reservoir studies to predict oil and gas in place needed to be calculated with more accuracy. The estimation of this important reservoir parameter is commonly determined by various well logs data and by applying some correlations that may not be so accurate in some real practical cases, especially for carbonate reservoirs. Since laboratory core analysis data have a high accuracy, in this study, it is attempted to use core and geological core description data to present an improved method to determine an optimized cementation factor (m) and a saturation exponent (n) in order to evaluate water saturation within carbonate reservoirs compared to default values (m=2, n=2, a=1) in a carbonate gas reservoir located in the Persian Gulf. Based on integrating core petrography and velocity deviation log (VDL), core samples were classified based on the type of porosity and geology description, and then by employing log-log plots of formation resistivity factor (FRF) versus porosity and formation resistivity index (FRI) versus water saturation, saturation parameters (m,n) were determined for each classification. Utilizing default and optimized values of saturation parameters, water saturation logs were obtained through different conductivity models by employing Multi min algorithm. Then, optimized water saturation was compared to core data. Error analysis showed that water saturation data resulted in optimized saturation parameters having a lower average error of 0.08 compared to the default ones with an average error of 0.14, and based on cumulative histogram, optimized water saturation data are in good agreement with the trend of core water saturation.