APPLICATION OF ANISOTROPIC ELASTIC INVERSION ON LITHO-FLUID FACIES PREDICTION USING MACHINE LEARNING IN X-FIELD, CENTRAL MALAY BASIN

Reservoir characterization is an essential step that contributes to the final drilling
decision in hydrocarbon-development projects. Seismic data is considered a crucial
source of data that helps to identify the litho-fluid facies distribution in reservoir rocks.
Accordingly, this study aims at inverting for the zero-offset acoustic and
shear-impedance volumes and transforming them into elastic attributes which act as
inputs to the facies model. The second objective is to predict the litho-fluid facies
distribution from the optimum set of lithology and fluid predictors. This study shows
how to reduce the ambiguity in facies discrimination and mitigate the effect of seismic
anisotropy in order to enhance the facies model’s accuracy.