Characterization and prediction of CO2 saturation resulting from modeling buoyant fluid migration in 2D heterogeneous geologic fabrics

• Saturation quantified for a broad range of clastic facies and depositional architectures.
• Presentation of two-dimensional invasion percolation saturation predictor model.
• Validation of IP predictor model with natural geologic specimens and field example.
• Implications of saturation variability for capacity estimation and seismic imaging.

Understanding the pore saturation of CO2 injected into heterogeneous rocks for permanent storage is a key challenge for constraining storage efficiency (capacity), plume migration extent, and seismic imaging. Much work on fluid flow and saturation has focused on traditional pressure-gradient driven flow represented by viscous multi-phase Darcy flow. Here we investigate CO2 saturations resulting from buoyancy-driven capillary flow, where buoyancy forces dominate viscous forces. These low capillary number flow regimes (Ca < 10−4) exist some distance from injection wells, potentially representing the majority of the storage domain during and after injection. We simulate CO2-brine buoyant displacement patterns using invasion percolation (IP) methods in various decimeter-scale, highly resolved heterogeneous 2D models. Different sedimentologic fabrics result in dramatic variability of total CO2 saturation. We seek a generalized predictive model that allows CO2 saturation resulting from capillary flow to be estimated reasonably for 2D domains from fundamental geologic and fluid properties. We focus on three metrics of pore threshold pressure in clastic porous materials: the central value (median; mean), the range (standard deviation), and the ratio of the horizontal to vertical correlation length. These are formalized in the description of 54 clastic facies and three fabrics. An ‘IP Saturation Predictor’ model is derived by linear interpolation of simulation results from synthetic stochastic models representing a wide range of facies and fabrics. To validate model results, we compare the predictive model with flow simulations in model domains extracted from highly resolved natural geologic sedimentary samples at similar resolution.