Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances

Seismic data are considered crucial sources of that help identify the litho-fluid facies distributions in reservoir rocks. However, different mostly have similar responses to seismic attributes. In addition, anisotropy negatively affects predictors extracted from data. Accordingly, this study aims at estimating zero-offset acoustic and shear impedances based on partial-stack inversion by two methods: statistical modeling a multilayer feed-forward neural network (MLFN). The resulting impedance volumes compared those obtained isotropic simultaneous using logs. best applied Thomsen’s equations solve for parameters Epsilon Delta. Finally, transformed into elastic properties which fluid predicted logistic regression decision tree algorithms. results MLFN show better matching with logs modeling.