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Shear and Stoneley wave velocities provide useful information for petrophysical and geomechanical studies of the reservoir formation. In this study, sonic Shear and Stoneley velocities were predicted from well log data using intelligent systems including: Fuzzy logic, neural networks, neuro-fuzzy, and support vector regression. After prediction, the proposed committee machine with intelligent systems combines the first three methods in performance view. Each of these selected intelligent systems has a weight factor and the optimal combination of the weights is derived by a genetic algorithm. The study was conducted on a case study from a carbonate reservoir in South Pars gas field. The results indicate the higher performance of the committee model compared to the individual and state-of-the-art methods.

energy sources, part a: recovery, utilization, and environmental effects

Estimation of shear and Stoneley wave velocities from conventional well data using different intelligent systems and the concept of committee machine: an example from South Pars gas field, Persian Gulf