The Rock Physics Analysis of Tight Sandstones Grain Size Classes with Image Based Petrophysics and Neural Net Modeling

The resolution of seismic data is a limitation when studying complex layered reservoirs. Integration of rock properties derived from open hole well logs and borehole image data can be used to bridge the resolution gap between seismic models and core scale physical measurements. Developed approach employs image-based petrophysics and neural network modeling to characterize textural and physical properties of tight sandstones in conjunction with rock-physics analysis. The workflow includes the following stages: 1) Standard petrophysical interpretation of well logs. 2) Neural network and image-based petrophysics analysis to generate grain-size classes. 3) Environmental correction and normalization of acoustic and density logs. 4) Fluid substitution to alleviate saturation effects on the acoustic and density logs. 5) Cross-plotting of elastic parameters with embedded grain-size classes to evaluate the grain-size classes separation before propagation into 3D seismic cube. This workflow was applied to Ordovician aged glacial fluvial tight sandstones that range in grain from very fine to very coarse, with occasional grits and/or pebbles. The workflow output enhances the seismic inversion and lithofacies distribution modelling. The results from 42 wells demonstrate the robust outcomes obtained and the methodology’s potential for improving the calibration and consistency of results from core to seismic scale physical modelling.