FFP is a valuable tool for understanding how to relate the elastic properties from seismic inversion to the rock properties on which drilling decisions are based.
FFP uses Bayesian Inference to discriminate between different lithotypes of rock type and fluid based on the elastic properties from a post-stack or simultaneous inversion.

Crossplotting the elastic properties from the well data and looking for clusters of points identifying discrete lithotypes determines how many different lithotypes can be modeled for any particular field.

Traditional histograms are transformed into a RockTrace Fluid Index, which gives greater separation between the lithotypes based on both the P- and S-impedance.

Once the lithotypes are transformed into RFI space for greater separation, a Bayesian Inference technique is used to assign a lithology probability for each of the defined lithotypes individually. A summary "most-likely" cube is also generated by sampling the individual probability cubes and finding the most likely lithotype for each sample.

Uncertainty can be tested by comparing the most-likely cube to "Second-most likely" volumes, which can also easily be created with FFP.