The Cretaceous Burro Canyon Formation in the southern Piceance Basin, Colorado, represents low sinuosity to sinuous braided fluvial deposits that consist of amalgamated channel complexes, amalgamated and isolated fluvial-bar channel fills, and floodplain deposits. Lithofacies primarily include granule-cobble conglomerates, conglomeratic sandstones, cross-stratified sandstones, upward-fining sandstones, and gray-green mudstones. To assess the effects of variable sandstone-body geometry and internal lithofacies and petrophysical heterogeneity on reservoir performance, conventional field methods are combined with unmanned aerial vehicle-based photogrammetry to create representative outcrop-based reservoir models. Outcrop reservoir models and fluid-flow simulations compare three reservoir scenarios of the Burro Canyon Formation based on stratigraphic variability, sandstone-body geometry, and lithofacies heterogeneity. Simulation results indicate that lithofacies variability can account for an almost 50% variation in breakthrough time (BTT). Internal channel-bounding surfaces reduce the BTT by 2%, volumetric sweep efficiency by 8%, and recovery efficiency by 10%. Three lateral grid resolutions and two permeability-upscaling methods for each reservoir scenario are explored in fluid-flow simulations to investigate how upscaling impacts reservoir performance. Our results indicate that coarsely resolved grids experience delayed breakthrough by as much as 40% and greater volumetric sweep efficiency by an average of 10%. Permeability models that are upscaled using a geometric mean preserve slightly higher values than those using a harmonic mean. For upscaling based on a geometric mean, BTTs are delayed by an average of 17% and the volumetric sweep efficiency is reduced by as much as 10%. Results of the study highlight the importance of properly incorporating stratigraphic details into 3D reservoir models and preserving those details through proper upscaling methods.
Retracted: Fluid Flow Simulations Based on an Equation-Solving Solution Gradient Strategy
