a new approach to measuring cementation factor by using an intelligent system

cementation factor is a critical parameter, which affects water saturation calculation. in carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. hence developing a reliable mathematical strategy to determine these properties accurately is of crucial importance. to this end, genetic algorithm pattern search is employed to find accurate cementation factor by using formation resistivity factor and the porosity obtained from laboratory core analyses with considering the assumption that tortuosity factor is not unity. subsequently, particle swarm optimization (pso) fuzzy inference system (fis) was used for the classification of cementation factor according to the predominated rock pore type by using the input variables such as cementation factor, porosity, and permeability to classify the core samples in three groups, namely fractured, interparticle, and vuggy pore system. then, the experimental data which was collected from sarvak formation located in one of the iran southwestern oil fields was applied to the proposed model. next, for each class, a cementation factor-porosity correlation was created and the results were used to calculate cementation factor and water saturation profile for the studied well. the results showed that the constructed model could predict cementation factor with high accuracy. the comparison between the model presented herein and the conventional method demonstrated that the proposed model provided a more accurate result with a mean square error (mse) of around 0.024 and led to an r2 value of 0.603 in calculating the water saturation.