Parallel Self Organizing Neural Network Estimation (PSONN) of Water Saturation Using Archie’s Formula in Sandstone Reservoirs

Determination of water saturation in sandstone is vital question to determine initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently, accuracy of Archie’s formula parameters affects rigorously water saturation values. Determination of Archie’s parameters a, m and n is proceeded by three techniques conventional, CAPE and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting an accepted value of Archie’s parameters and consequently reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique and 3-D technique and then calculation of water saturation using current. Using the same data, hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and to predict water saturation. Results have shown that estimated Arche’s parameters m, an and n are highly accepted with statistical analysis indicating that PSONN model has lower statistical error and higher correlation coefficient. This study was conducted using high number of measurement points for 144 core plugs from sandstone reservoir. PSONN algorithm can provide reliable water saturation values and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculation of water saturation profiles.