Quantifying spatial heterogeneity of fracture networks in high porosity sandstone using novel digital data acquisition methods
We captured fracture arrays in high porosity sandstones using Terrestrial Laser-Scanning and Real Time Kinematic GPS to build up a 3-D virtual outcrop. It allows us to interpolate fracture surfaces with high accuracy on a desktop PC. This virtual dataset is calibrated with a field dataset measured with compass/clinometer sequentially along the outcrop. Both datasets have two natural clusters showing NE dipping and SW dipping fracture sets. Standard statistical inference is used to quantify the spatial heterogeneity of the fracture orientations. Significant variations are resolved in a different bin size to model the distribution patterns at different scales. The results show that the virtual dataset gives us the same bimodal distribution pattern as the field dataset, however, there are more significant variations at smaller scales than at the larger scale. From east to the west along the outcrop, the NE dipping fracture set rotates horizontally whereas the SW dipping fracture set rotates vertically by 15-20. This outcrop analogue case study reveals that there is greater uncertainty when down-scaling compared to up-scaling.