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Reservoir characterization using support vector machines

Wong, K.W., Ong, Y.S., Gedeon, T. and Fung, C.C. (2005) Reservoir characterization using support vector machines. In: 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’05), 28-30 Nov. 2005, Vienna, Austria.

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    Link to Published Version: http://dx.doi.org/10.1109/CIMCA.2005.1631494
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    Abstract

    Reservoir characterization especially well log data analysis plays an important role in petroleum exploration. This is the process used to identify the potential for oil production at a given source. In recent years, support vector machines (SVMs) have gained much attention as a result of its strong theoretical background. SVM is based on statistical learning theory known as the Vapnik-Chervonenkis theory. The theory has a strong mathematical foundation for dependencies estimation and predictive learning from finite data sets. This paper presents investigation on the use of SVM in reservoir characterization. Initial results show that SVM can be an alternative intelligent technique for reservoir characterization.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: © 2005 IEEE.
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/987
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