Estimation of Archie parameters by a novel hybrid optimization algorithm

Abstract

Abstract Archie parameters play a critical role in accurately identifying water saturation for a given reservoir condition. Due to interdependence of these parameters, it is difficult to estimate them accurately. In order to achieve more accurate parameters, we model a non-convex optimization problem based on the core Archie parameters’ estimate (CAPE). Then we present a new hybrid global optimization method to solve this non-convex problem. The hybrid technique has the features of both fast local convergence in interior point method and global convergence in Firefly algorithm (FA). Finally, our method was implemented to determine Archie parameters and some comparisons are done among two deterministic techniques and four population-based algorithms. Water saturation profiles were generated using the different Archie parameters determined by six techniques. These profiles have shown a significant difference in water saturation values between CAPE methods and population-based methods. These results highlight that our proposed algorithm performed better than conventional CAPE and three dimension (3D) method for reservoirs to calculate the water saturation values due to more accurate Archie parameters achieved by our method.