Seismic Multi-attributes Analysis to Predict Lithology Distribution and Porosity

The method that is used in this research is seismic multi-attributes analysis which predicts reservoir properties using several seismic attributes. This analysis sought a relationship between the logs and the seismic data on the location of wells and use those relationships to predict or estimate the volume of log property on all locations on the seismic volume. Well logs and post stack 3D seismic data with normal polarity and zero phase were utilized for this research. This research was started with log properties crossplot to search for the sensitive parameters. Wavelet extraction then used to find the best wavelet to be used in well-seismic tie. Well-seismic tie was done to tie the well data (depth scale) with the seismic data (time scale). The inversion method then used to produce AI volume which is utilized as an external attribute for seismic multi-attributes analysis. The results of seismic multi-attributes analysis are pseudo gamma ray, pseudo density, and pseudo porosity volume. These volumes are used to predict the lithology distribution and porosity of sandstones “A” in Bekasap Formation. From these pseudo volumes, horizon slice maps at T_BK Sand “A” and B_BK Sand “A” windows were created for each volume.