Hybrid Artificial Intelligent Approach for Determination of Water Saturation using Archie’s Formula in Carbonate Reservoirs

Abstract

The challenge to determine the accurate water saturation is still faced Petroleum Engineering. The difficulty of this problem will be increase if we deal with carbonate rocks. There are some available techniques used to determine the water saturation. However, the accuracy of those techniques has become unable to find the best results. Several available techniques have been used to estimate water saturation such as conventional, CAPE (a, m, n), CAPE (1, m, n) and 3D methods. Currently, the achievements of Artificial Intelligent (AI) techniques alone open the door to use the hybrid system such as (PSONN). In this model, the Particle Swarm Optimization (PSO) technique is employed to search for optimal connection weighs and thresholds for the neural networks (NN), then the back-propagation learning rule and training algorithm is used to adjust the final weights. A total of about 383 data points obtained from the laboratory measurements of electrical properties from carbonate core plugs of Middle East reservoir were used for the implementation of the proposed technique. Statistical analysis and comparative study show that the performance of PSONN model is the best one with lower root mean square error (0.092) and higher accuracy of correlation coefficient (0.95) than those obtained with previous methods. Results showed that the new hybrid PSONN model outperforms some available methods and overcome the weakness if we use AI alone. From error analysis, it is found that CAPE and 3-D and PSONN methods ensure minimum error of water saturation values.