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A modular signal processing model for permeability prediction in petroleum reservoir

Wong, K.W. and Gedeon, T. (2000) A modular signal processing model for permeability prediction in petroleum reservoir. In: Neural Networks for Signal Processing - Proceedings of the IEEE Workshop, Sydney, NSW, 11-13 December 2000 pp. 906-915.

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    Link to Published Version: http://dx.doi.org/10.1109/NNSP.2000.890171
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    Abstract

    The use of Artificial Neural Network (ANN) especially Backpropagation Neural Network (BPNN) has been a promising tool for well log analysis in predicting permeability. However, due to the range of permeability data, it is normally converted using logarithmic transform before being used for data analysis by the BPNN. This has an impact on the accuracy of the permeability prediction. This paper suggests a model for improving the permeability prediction. It first divides the whole sample space of the permeability values according to their logarithmic region, and then generates individual BPNNs for each logarithmic region. In this initial study, Learning Vector Quantization (LVQ) is used for this Purpose for separating the data. After that, each region is then handled by each BPNN. This method not only preserves the resolution of the permeability, but at the same time, increase the prediction accuracy. The contributions of this paper are to identify the problems in the signal processing of permeability prediction, and exploit new direction of improving permeability prediction using well logs.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: © 2000 IEEE
    URI: http://researchrepository.murdoch.edu.au/id/eprint/997
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