Predicting Porosity by Multivariate Regression and Probabilistic Neural Network using Model-based and Coloured Inversion as External Attributes: A Quantitative Comparison

The acoustic impedance (AI) inversion aims to obtain a high-resolution impedance volume by integrating well-log and band-limited seismic data. Two AI inversion schemes: the coloured inversion (CI) and the model-based inversion (MBI) are utilized to characterize possible sand channel from the post-stack seismic section and log data from 13 wells from the Blackfoot region, Alberta, Canada. The results from analyses indicate that both the model-based and coloured inversion methods provide mutually consistent impedance volumes with an average correlation coefficient of 0.986 and 0.886 for MBI and CI, respectively. Both inversions show low-impedances ranging from 6750-7350m/s*g/cc between 1060ms and 1065ms time interval which is interpreted as a sand channel. The slice of the acoustic impedance variation along all cross line and inline validates the presence of low impedances along the interpreted sand channel. Thereafter, the multivariate regression and the Probabilistic Neural Network (PNN) are employed to predict porosity volumes using CI and MBI inverted impedance as external attributes. The cross-plots between predicted porosities and actual porosities using multivariate regression and PNN algorithms indicate that PNN produces better statistical estimates of porosity distribution compared to those predicted from the multivariate regression. Both methods show high porosity values along the sand channel. The maximum porosity in the sand channel is 18% when MBI derived impedance is used as an external attribute while it is 16% in the case of CI. The results suggest that given seismic and well log data for a region, a combination of model-based inversion and PNN can produce a more reliable estimate of the petrophysical properties of the sub-surface.