On the evaluation of Representative Elementary Area for porosity in shale rocks by Field Emission Scanning Electron Microscopy

Field Emission Scanning Electron Microscopy (FESEM) is commonly used to characterize shales at the nanoscale, but nevertheless, its use in quantitative analysis is still limited. High-resolution images over large areas can be acquired by FESEM and dedicated software, identifying pores with diameters around 20 nm. Although from image analysis is possible to account for a large number of pores, a crucial question is whether these images are representative of larger areas of the rock. The evaluation of Representative Elementary Area (REA) in shale is essential to perform a reliable analysis of the pore space and porosity. The intrinsic heterogeneity of the system sets the requirement for the definition of the minimum area where the property can be determined and the largest area that it represents. This paper shows that porosity data computed in different randomly selected areas of the same sample exhibit a large spread. A novel method to identify REA based on the selection of areas with similar mineralogy, named Z-contrast criterion, is proposed. This method leads to a noticeable lower spread on the porosity values. Porosity distribution between Organic Matter (OM) and minerals by a trainable machine learning software is also determined and compared with independent measurements. [Display omitted] •Random selection of areas for computing porosity leads to high variability data.•Selection of REA should include an extra criterion.•Z-contrast criterion reduces the spread of porosity data and gives a reliable REA.•Image processing by machine learning allows OM and NOM porosity segmentation.•Image analysis allows a reliable determination of OM content.