TY  - JOUR
IS  - 15
N1  - cited By 0
A1  - Gouda, M.F.
A1  - Abdul Latiff, A.H.
A1  - Moussavi Alashloo, S.Y.
Y1  - 2022///
SN  - 20763417
TI  - Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
ID  - scholars16519
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136954786&doi=10.3390%2fapp12157754&partnerID=40&md5=58919e70b662a93dca58b6f1e1ad641d
VL  - 12
JF  - Applied Sciences (Switzerland)
PB  - MDPI
N2  - Seismic data are considered crucial sources of data that help identify the litho-fluid facies distributions in reservoir rocks. However, different facies mostly have similar responses to seismic attributes. In addition, seismic anisotropy negatively affects the facies predictors extracted from seismic data. Accordingly, this study aims at estimating zero-offset acoustic and shear impedances based on partial-stack inversion by two methods: statistical modeling and a multilayer feed-forward neural network (MLFN). The resulting impedance volumes are compared to those obtained from isotropic simultaneous inversion by using impedance logs. The best impedance volumes are applied to Thomsenâ??s anisotropy equations to solve for the anisotropy parameters Epsilon and Delta. Finally, the shear and acoustic impedances are transformed into elastic properties from which the facies and fluid distributions are predicted by using the logistic regression and decision tree algorithms. The results obtained from the MLFN show better matching with the impedance and facies logs compared to those obtained from isotropic inversion and statistical modeling. © 2022 by the authors.
ER  -