A New Approach to Measuring Cementation Factor by Using an Intelligent System

Cementation factor is a critical parameter, which affects water saturation calculation. In carbonaterocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associatedwith high inaccuracy. Hence developing a reliable mathematical strategy to determine these propertiesaccurately is of crucial importance. To this end, genetic algorithm pattern search is employed to findaccurate cementation factor by using formation resistivity factor and the porosity obtained fromlaboratory core analyses with considering the assumption that tortuosity factor is not unity.Subsequently, particle swarm optimization (PSO) fuzzy inference system (FIS) was used for theclassification of cementation factor according to the predominated rock pore type by using the inputvariables such as cementation factor, porosity, and permeability to classify the core samples in threegroups, namely fractured, interparticle, and vuggy pore system. Then, the experimental data whichwas collected from Sarvak formation located in one of the Iran southwestern oil fields was applied tothe proposed model. Next, for each class, a cementation factor-porosity correlation was created andthe 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 thatthe proposed model provided a more accurate result with a mean square error (MSE) of around 0.024and led to an R2 value of 0.603 in calculating the water saturation

Keywords:

Cementation Factor , Carbonate Reservoir , Genetic Algorithm , Particle Swarm Optimization , Fuzzy Logic