Representative elementary volume estimation for porosity, moisture saturation, and air-water interfacial areas in unsaturated porous media: Data quality implications

[1] Achieving a representative elementary volume (REV) has become a de facto criterion for demonstrating the quality of lCT measurements in porous media systems. However, the data quality implications of an REV requirement have not been previously examined. In this work, deterministic REVs for porosity, moisture saturation (SW), and air-water interfacial area (AI) were estimated using a set of 49 lCT images of eight unsaturated homogeneous porous media with heterogeneity in moisture distributions present in varying degrees. Estimated porosity REVs were <8 mm for all cases, smaller than typical lCT image sizes ( 100 mm). Estimated SW and AI REVs were <55 mm for cases with homogeneous moisture distributions but could not be estimated for cases with heterogeneous moisture distributions, due to the absence of a distinct ‘‘REV plateau’’ within the maximum imaged volume. Conventionally, SW and AI data from such non-REV cases would be excluded. The implications of excluding data on the basis of REV were examined by comparing AI-SW data measured on image windows of increasing size against the expected linear AI-SW relationship. At measurement scales exceeding porosity REV, random fluctuations in AI-SW data were excluded, even for cases containing heterogeneous moisture distributions. In contrast, requiring measurement scales to exceed SW and AI REV appeared overly restrictive and resulted in visible loss of reliable AI-SW data. We attribute these findings to overestimation of REVs due to inherently problematic estimation of deterministic REVs in real systems. Implications of these findings for ensuring lCT data quality and the efficient use of lCT data are discussed.