Estimation of cementation factor in carbonate reservoir by using genetic fuzzy inference system

Abstract

Water saturation is a key parameter in reservoir engineering to calculate the volume of hydrocarbon in reservoirs. The first attempt to estimate water saturation using well log data was implemented by Archie in 1942 for a clean sandstone reservoir. This method requires laboratory measurement of cementation factor. This parameter has the most impression on water saturation calculation compared to the other parameters, which is nearly constant in homogenous sandstone reservoirs. However, due to high variation of cementation factor along depth of wellbore in carbonate reservoirs due to rock’s nature, it is incorrect to assign a constant value to cementation factor. On the other hand, experimental core analysis to determine cementation factor values is an expensive and time-consuming work, and it is impossible to calculate this parameter in laboratory for the whole depth of a drilled wellbore. In industrial applications, using a constant cementation factor can lead to erroneous calculations of water (and hence oil) saturations. Also, previous conventional methods estimating cementation factor from logging data are not often sensitive to pore system of rock and generate a massive source of error. In this study, a new approach to estimate cementation factor using a genetic Mamdani fuzzy inference system was implemented for a case study in Sarvak Formation located in Zagros Basin which is mostly composed of pure limestone. The final results show a high exactness of the proposed model estimating cementation factor with an R-squared of 0.864 and a mean squared error of 0.01899 .