Prediction of Shear Wave Velocity from Petrophysical Data Utilizing Intelligent Systems

Category Geophysic
Group GSI.IR
Location 23th symposium on geosciences
Holding Date 05 May 2008

      Reservoir characterization is a prerequisite for oil and gas field development. Shear wave velocity associated with compressional wave velocity can provide the accurate data for geophysical study of the reservoir. These so called petroacoustic studies which have important role in characterization of the reservoir such as lithology determination, identifying pore fluid type and geophysical interpretation.
   In this study, fuzzy logic, nero-fuzzy and artificial neural networks were used as intelligent tools to predict shear wave velocity from petrophysical data. Petrophysical data from two wells were used for constructing intelligent models in a sandstone reservoir. A third well from the field was used to evaluate the reliability of the models.
   Results show that intelligent models have been successful in prediction of shear wave velocity. However fuzzy models to some extent have been more successful than others.
   Keywords: Shear wave velocity, Fuzzy logic, fuzzy inference system, nero fuzzy, artificial neural network, petrophysical data

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