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dc.contributor.authorRezaee, M. Reza
dc.contributor.authorKadkhodaie Ilkhchi, A.
dc.contributor.authorBarabadi, A.
dc.date.accessioned2017-01-30T14:29:50Z
dc.date.available2017-01-30T14:29:50Z
dc.date.created2008-11-26T02:24:49Z
dc.date.issued2007
dc.identifier.citationRezaee, 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.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/39059
dc.identifier.doi10.1016/j.petrol.2006.08.008
dc.description.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.

dc.publisherElsevier
dc.subjectfuzzy logic
dc.subjectCarnarvon Basin
dc.subjectneuro-fuzzy
dc.subjectAustralia
dc.subjectpetrophysical data
dc.subjectartificial neural network
dc.subjectShear wave velocity
dc.titlePrediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia
dc.typeJournal Article
dcterms.source.volume55
dcterms.source.startPage201
dcterms.source.endPage212
dcterms.source.titleJournal of Petroleum Science and Engineering
curtin.note

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

curtin.departmentDepartment of Petroleum Engineering
curtin.identifierEPR-2487
curtin.accessStatusOpen access


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