A STATISTICAL METHODOLOGY FOR DERIVING RESERVOIR PROPERTIES FROM SEISMIC DATA

The use of seismic data to better constrain the reservoir model betwee n wells has become an important goal for seismic interpretation, We pr opose a methodology for deriving soft geologic information from seismi c data and discuss its application through a case study in offshore Co ngo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing th e traces at the reservoir level. We built statistical relationships be tween seismic attributes and reservoir properties from a calibration p opulation consisting of wells and their adjacent traces. The correlati on studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression betwe en the canonical seismic variables and the reservoir properties, whene ver they are predictable. In the case study, we predicted estimates an d associated uncertainties on the lithofacies thicknesses cumulated ov er the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological predictio n results in the cases of a calibration on the whole data set and a ca libration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly bec ause the large scale geological variations (and associated seismic one s) over the field can be accounted for.