Registration and Fusion of Backscattered Electron Images and Energy Dispersive Spectroscopy Images on Siliciclastic Rock Samples for Petrophysical Calculations

Abstract

High resolution Backscattered Electron images (BSE) can be used to extract textural information such as grain and pore size; specific surface area; and so forth. Based on this information porosity and permeability can be estimated. On the other hand, low resolution Energy Dispersive Spectroscopy Imaging (EDS) provides valuable information about the mineralogy and chemical composition of the rock samples. Scanning Electron Microscopy (SEM) instruments are capable of generating BSE images of core and cuttings samples in a relatively short period of time at very high resolutions. The story is different if EDS images are also acquired. For example EDS images at 20 μm point spacing usually do not take longer than 30 minutes to be captured, but if point spacing is reduced to 10 μm, then measurement time increases approximately by 4 times. This paper proposes a new methodology to combine high resolution BSE images with low resolution EDS images and to use the combined textural and mineralogical information in order to improve grain segmentation, grain size calculations as well as estimating porosity and permeability in rock samples. This methodology starts with the acquisition of the BSE and EDS images looking for an optimal relationship between acquisition time and image quality. Secondly the registration and fusion of the two images is performed and advanced image processing techniques are applied to extract information that corresponds to underlying physical characteristics such as porosity and permeability. Results of this methodology are encouraging, 4 to 5 μm point spacing BSE images have been registered and fused with 20 μm EDS images and excellent grain segmentation has been achieved. Calculated porosity values show a good match with helium porosity core data and estimated permeability using the Kozeny-Carman equation gives results in the same order of magnitude. This new approach saves time, operating costs and enable geoscientists to collect valuable rock data at the micro-scale level. Furthermore, this information can be used to estimate important rock properties when integrated with other data sources to improve reservoir characterization especially in cases where information is scarce or difficult to obtain.