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    A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters

    Access Status
    Fulltext not available
    Authors
    liu, J.
    Xu, Honglei
    Wu, G.
    Teo, Kok Lay
    Date
    2015
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    liu, J. and Xu, H. and Wu, G. and Teo, K.L. 2015. A New Hybrid Optimization Algorithm for the Estimation of Archie Parameters, in Honglei Xu, Song Wang, Soon-Yi Wu (ed), The 9th International Conference on Optimization: Techniques and Applications (ICOTA9), Dec 12 2013, pp. 137-154. Tai pei, Taiwan: Springer.
    Source Title
    Optimization Methods, theory and Applications
    Source Conference
    The 9th International Conference on Optimization: Techniques and Applications (ICOTA9)
    Additional URLs
    http://link.springer.com/chapter/10.1007/978-3-662-47044-2_7#page-1
    ISBN
    9783662470435
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/10930
    Collection
    • Curtin Research Publications
    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.

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