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A large borehole lithology dataset from the shallowly buried alluvial aquifer of the Brenta River Megafan (NE Italy) is used in this paper to model hydrofacies with three classical geostatistical methods, namely the Object-Based Simulation (OBS), the Sequential Indicator Simulation (SIS), and the Truncated Gaussian Simulation (TGS), and rank alternative output models. Results show that, though compromising with geological realism and rendering a noisy picture of subsurface geology, the pixel-based TGS and SIS are better suited than OBS for their ease of conditioning to closely spaced boreholes, especially in fine-scale simulation grids. In turn, SIS appears to provide better prediction and less noisy hydrofacies models than TGS without requiring assumptions about relationship among operative facies, which makes it particularly suited for use with large borehole lithology datasets lacking detail and quality consistency. Flow simulation on a test volume constrained with numerous boreholes indicates the SIS hydrofacies models feature well-connected sands forming relatively fast flow paths as opposed to TGS models, which instead appear to carry a more dispersed flow. It is shown how such a difference primarily relates to ‘noise’, which in TGS models is so widespread to translate into a disordered spatial distribution of K and, consequently, a nearly isotropic simulated flow.

Three Geostatistical Methods for Hydrofacies Simulation Ranked Using a Large Borehole Lithology Dataset from the Venice Hinterland (NE Italy)