Towards four phase autosegmentation and microporosity quantification

The topic of digital rock physics including experimental imaging techniques such as computed tomography (CT) is on the rise in oil and gas research. The aim is to map the pore architecture and subsequently to model multiphase flow with the prospect of improving and developing enhanced oil recovery methods. The initial computational step to construct the pore network architecture is the so-called segmentation of CT-tomograms into the constituent phases. We have probed two different packages, Avizo and MANGO to address pros and cons of the packages and the segmentation process itself. Segmentation involves a substantial amount of manual assignment of grayscale intensity thresholds to the given phases. This is a significant challenge in particular as the process is based on qualitative judgment and hence subjected to a substantial human bias. Importantly, in carbonates, where the very small pores are unresolvable under the experimental conditions, segmentation becomes particularly challenging. In order to be able to make consistent, unbiased and quantitative segmentation of a full high-resolution tomogram we have developed a fully deterministic autosegmentation algorithm. The segmentation results in quantification of the macroporosity, microporosity and of specific minerals such as pyrite. The autosegmentation algorithm is based on an advanced statistical analysis of the global and local intensity distributions of the tomogram. The method involves local normal distribution fitting, differential curve intersection analysis, density function estimation and subsequent derivative analysis. The statisticsbased definition of microporosity represents in itself a novel approach to the characterize microporosity, where microporosity identification and quantitation are based on properties of the well-defined region of intensity distributions of the present grains and macroporosity. Identification of special minerals such as pyrite is based on statistical outlier analysis. In the paper we present (1) an algorithm to perform automated CT-image segmentation, (2) a method to quantitatively distinguish microporosity from macroporosity, (3) a method to identify special minerals and (4) a method to estimate the relative presence of grain, macroporosity, microporosity and selected special minerals.