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

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 seism...

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Bibliographic Details
Published in:Applied sciences 2022-08, Vol.12 (15), p.7754
Main Authors: Gouda, Mohammed Fathy, Abdul Latiff, Abdul Halim, Moussavi Alashloo, Seyed Yasser
Format: Article
Language:English
Subjects:
Acoustic impedance
Acoustic properties
Anisotropy
Elastic properties
Impedance
Impedance matching
Inversion
Lithology
Mathematical models
Multilayers
Neural networks
parameter estimation
Permeability
Physics
Reservoirs
rock physics
Seismic response
Shear
Statistical analysis
Statistical methods
Statistical models
Symmetry
Velocity
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Summary: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 Thomsen’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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12157754