Abstract
Shear wave velocity ( Vs) associated with compressional wave velocity ( Vp) can provide accurate data for geophysical study of a reservoir. These so called petroacoustic studies have important role in reservoir characterization objectives such as lithology determination, identifying pore fluid type, and geophysical interpretation. In this study, fuzzy logic, neuro-fuzzy and artificial neural network approaches were used as intelligent tools to predict Vs from conventional log data. The log data of two wells were used to construct intelligent models in a sandstone reservoir of the Carnarvon Basin, NW Shelf of Australia. A third well was used to evaluate the reliability of the models. The results showed that intelligent models were successful for prediction of Vs from conventional well log data. In the meanwhile, similar responses from different other intelligent methods indicated their validity for solving complex problems.
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