A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters

Jianjun Liu (), Honglei Xu, Guoning Wu and Kok Lay Teo
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Jianjun Liu: China University of Petroleum, College of Science
Honglei Xu: Curtin University, Department of Mathematics and Statistics
Guoning Wu: China University of Petroleum, College of Science
Kok Lay Teo: Curtin University, School of Mathematics and Statistics

Chapter Chapter 7 in Optimization Methods, Theory and Applications, 2015, pp 137-154 from Springer

Abstract: Abstract Archie formula, which contains three fundamental parameters (a, m, n), is the basic equation to compute the water saturation in a clean or shaly formation. These parameters are known as Archie parameters. To identify accurately the water saturation for a given reservoir condition, it depends critically on the accurate estimates of the values of Archie parameters (a, m, n). These parameters are interdependent and hence it is difficult to identify them accurately. So we present a new hybrid global optimization technique, where a gradient-based method with BFGS update is combined with an intelligent algorithm called Artificial Bee Colony. This new hybrid global optimization technique has both the fast convergence of gradient descent algorithm and the global convergence of swarm algorithm. It is used to identify Archie parameters in carbonate reservoirs. The results obtained are highly satisfactory. To further test the effectiveness of the new hybrid global optimization method, it is applied to ten non-convex benchmark problems. The outcomes are encouraging.

Keywords: Archie parameters; Hybrid global optimization; ABC algorithm; Gradient-based method (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-662-47044-2_7

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DOI: 10.1007/978-3-662-47044-2_7

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