INFERENCE OF SHEAR WAVE VELOCITIES (VS) USING FUZZY NEURAL NETWORKS

Abstract

 In this work we use neuro fuzzy system (NFS) in order to obtain inference equations of shear velocity (Vs) of a well in terms of the logarithmic of its porosity (φ), its water saturation (Sw) and its clay volume (Vsh) data. The data belong to a well of the Guafita Field located in the Sub-basin of Apure, Venezuela. For training of NFS were used as input values: φ, Sw, Vsh; and Vs a s output. The training was made with multiple combinations of the independent parameters φ, Vsh and Sw. The results suggest that the use of the three types of registers simultaneously improves notoriously Vs inference, compared with the use of only one of them or combinations of two parameters. The number of fuzzy rules was changed for all combinations of parameters. It was observed that increasing the number of rules does not produce a marked improvement in the results.