The PGS lithology/fluid prediction workflow utilizes a Bayesian supervised classification scheme to make quantitative predictions based on inverted seismic data and depth-dependent, statistical rock physics models over a target zone of interest. The process generates lithology and fluid probability volumes.
Bayesian lithology-fluid prediction using a depth dependent rock physics model. The red body indicates the maximum probability of having a hydrocarbon sand.
Stochastically modeled multivariate probability density functions (PDFs), which account for the uncertainty in the target lithology/fluid combinations, are calculated at each depth level of interest.
These are then quantitatively compared to equivalent inverted data to make predictions. Individual lithology and fluid probability volumes are generated based on the correlation between the predicted PDFs and the observed values from the inversion.
A most likely lithology/fluid probability volume is calculated and the most probable lithology and fluid in each trace location is also derived.