A multi-scale analysis bridging the gap from centimetres to reservoir simulation cell size for heterogeneous tidal reservoirs

Accurate subsurface characterization is essential for energy transition technologies including geothermal systems, carbon capture and storage (CCS), and hydrogen storage. Reservoir models face a critical challenge: core measurements of petrophysical properties at centimetre scale are used to populate simulation cells which are typically 10-100 m, creating a very large scale gap (10¹⁰). Inappropriate upscaling methods lead to systematic errors in flow predictions and fail to preserve the impact of heterogeneity at different levels, particularly in heterolithic depositional environments such as tidal systems where mud drapes create extreme vertical flow barriers. Here, we present a novel approach for multi-scale study that uses virtual outcrop analogues including standard virtual outcrops and high-resolution mini-models collected using smartphone-based lidar. The Sego Sandstone Formation from Sego Canyon (the Book Cliffs, Utah, USA) serves as the case study, representing a tide- and wave-influenced shoreline succession. These deposits serve as analogues to the Garn Formation in the mid-Norwegian Continental Shelf and similar tidal reservoirs.

The workflow comprises three steps. The first step includes small-scale models (1-5 m extent; 1 cm grid resolution) where virtual mini-model data captures centimetre-scale sedimentary heterogeneity. These models were then upscaled to 1 m horizontal and 0.1 m vertical resolution, followed by statistical regression analysis. The second step involves meso-scale models (25×25×15 m model size; 1×1×0.1 m cell size) where these regression relationships are applied, enabling systematic testing of upscaling methods under controlled conditions. These meso-scale models were then upscaled to 25 m horizontal and 1 m vertical resolution, representing typical cell dimensions for reservoir models. Statistical and regression analysis were repeated to derive reservoir-scale upscaling parameters. The final step comprises macro-scale reservoir models with outcrop-scale dimensions and 25×25×1 m cell size, applying validated upscaling parameters from the previous step. Outcomes were compared with original small-scale data to quantify the impact of multi-resolution heterogeneity and identify which geological features have the most influence on upscaled values.

Results demonstrate that depositional architecture fundamentally controls upscaling behaviour, with heterogeneity significantly affecting permeability predictions at all levels. Clean sand facies (tidal bars, shoreface) show predictable behaviour with minimal scale effects on horizontal permeability and moderate vertical anisotropy controlled primarily by cross-bedding dip. Heterolithic facies (inter-bar, offshore transition zones) display moderate horizontal permeability variation but extreme vertical permeability reduction due to continuous mud drapes creating severe vertical flow barriers. Overall, permeability shows complex behaviour at different scales, which cannot be captured by placing data from centimetre-scale core plug measurements directly into simulation cells - a critical limitation for subsurface studies. This methodology is transferable across all depositional environments and directly applicable to energy transition projects requiring accurate multi-resolution flow predictions.