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Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia

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Authors
Rezaee, M. Reza
Kadkhodaie Ilkhchi, A.
Barabadi, A.
Date
2007
Type
Journal Article

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Rezaee, M.R. and Kadkhodaie Ilkhchi, A. and Barabadi, A. 2007. Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia. Journal of Petroleum Science and Engineering. 55 (3/4): 201-212.
Source Title
Journal of Petroleum Science and Engineering
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Department of Petroleum Engineering
Remarks

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Petroleum Science and Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Petroleum Science and Engineering, Vol. 55, issue 3/4, 2007, http://dx.doi.org/ 10.1016/j.petrol.2006.08.008

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Abstract

Shear wave velocity associated with compressional wave velocity can provide the accurate data for geophysical study of a reservoir. These so called petroacoustic studies have important role in reservoir characterization such as lithology determination, identifying pore fluid type, and geophysical interpretation. In this study, a fuzzy logic, a neuro-fuzzy and an artificial neural network approaches were used as intelligent tools to predict shear wave velocity from petrophysical data. The petrophysical data of two wells were used for constructing intelligent models in a sandstone reservoir of Carnarvon Basin, NW Shelf of Australia. A third well of the field was used to evaluate the reliability of the models. The results show that intelligent models have been successful for prediction of shear wave velocity from conventional well log data.