Warming in the Arctic is occurring at twice the rate of the global average, resulting in permafrost thaw and a restructuring of the Arctic hydrologic cycle as indicated by increased stream discharge during low-flow periods. In these cold regions, permafrost thaw is postulated to increase low-flow discharge, or baseflow, through either:

Summary Rock typing based on mineralogical, hydraulic, or petrophysical similarities is important to reservoir characterization and simulation. In the literature, classifying rocks using single-phase data has been widely studied. Most methods use porosity and permeability measurements to identify rocks with similar characteristic pore sizes. In this study, we invoke concepts from critical-path analysis (CPA) and propose a new rock-typing method on the basis of two-phase flow data, such as water relative permeability krw. We classify rocks based on their similarities in the critical pore radius rc at the same effective water saturation Se. For this purpose, we first convert the Sw−krw plots to Se−rc curves and then apply a curve clustering method to identify similar rocks. To evaluate the proposed approach, we simulated flow in pore networks with many different pore-scale properties. By varying the pore-throat size distribution, contact angle, pore coordination number, pore-shape distribution, and clay content, we generated a wide range of pore networks. Overall, two-phase flow in 240 pore networks were simulated. In addition to synthetic pore networks, pore networks were generated based on properties of Berea, Mt. Simon, and Fontainebleau sandstones. By analyzing the single-phase simulation results, we identified 8 and 15 rock types using the porosity-formation factor and reciprocal formation factor-permeability data, respectively. However, using the two-phase data, we detect 12 rock groups.

Grouping soils based on similarities in their textural, taxonomic, and/or structural properties has broad applications to pedology, hydrology, and soil science. In this study, we present a new approach for classifying soils using hydraulic conductivity data. We apply concepts from critical path analysis and calculate critical pore sizes at various water saturations from the unsaturated hydraulic conductivity curves. Soils with similar critical pore size at the same effective water saturation are then grouped into the same class. To demonstrate the practical application of the proposed soil classification method, we use 102 samples including nine soil textures from the UNSODA database. Applying a curve clustering method, we find eight different soil classes within the studied data set.

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