Mapping petrophysical properties with seismic inversion constrained by laboratory based rock physics model

Estimation of reservoir properties from seismic data suffers from non-unique solutions. A workflow based on the numerical reformulation of a laboratory-based rock physics model may reduce the non-uniqueness. This study attempts to integrate seismic and well log data of relatively unexplored parts of Upper Assam (UA) basin using inversion driven by laboratory based rock physics model. The laboratory-based rock physics model was developed based on experimental measurements conducted on rock cores from Tipam and Barail formations of the basin. Seismic inversion analysis was performed in OpendTect, an open-source software on post-stack seismic data to derive the acoustic impedance (AI) using coloured inversion. A multilayered feed-forward neural network was developed to spatially populate different petrophysical properties. Laboratory-based correlations between AI, density, porosity were utilised for the AI model from which velocity was computed using multivariate rock physics equation. This derived velocity value was transformed to AI and subsequently trained with well log to populate density (2.23-2.73 gm/cc) and porosity (7-28%) for the entire survey area. A reasonable to high correlation is obtained between bulk density and porosity derived by NN using well log and that derived by laboratory-based model (r = 0.78, 0.91 for Barail and 0.95, 0.94 for Sylhet formation). Thus, integrating datasets of different scale from seismic to core with well log data using neural network helps to derive more realistic models that helps in quantitative decision analysis.