On the Use of Particle-Size-Distribution Data for Permeability Prediction

Abstract

Summary Particle-size distribution (PSD) is a list of values that defines the relative amount of particles present according to the size in a sample. The PSD of the McMurray Formation sediments characterizes rock granulometry and is a fundamental indicator of the nature of the sediment. The size distribution of the component solid particles in the McMurray Formation sediments relates to their porosity; volume of water and bitumen contained within the pore space; and the depositional environment, including lithological association, stratigraphy, areal distribution, and associated physical processes. PSD is known to be a significant factor for evaluating bitumen recovery from an oil-sand mine. This is because presence of fines (evaluated by PSD analysis) affects the hot-water-separation process and processing-plant recovery prediction and provides grade control. Presence of more fines translates into lower recovery from commercial oil-sand processing. In this study, we investigate whether the PSD should be also considered a critical parameter for evaluation and estimation of permeability of an oil-sand reservoir. We show, by use of the data from Cenovus Energy's Telephone Lake lease, that there is a strong relationship between permeability and PSD data. We also show that the information provided by the PSDs for permeability prediction is more significant than that inferred from a simple porosity/permeability relationship. Subsequently, we comment on permeability modeling by use of PSD data and list the techniques available for cleaning and modeling of multivariate PSDs. We document a methodology for modeling of PSDs and provide a work flow for incorporating these data in improved understanding and modeling of permeability and its distribution.

Predicting sand size distribution based on well logs of east Malaysia basins

“…Berg This correlation links petrological variables such as the grain size, shape, and sorting. Unlike Krumbein and Monk, Berg considers porosity as one of the parameters in the model, making it more accurate than Krumbein and Monk's [18,19]. Berg's correlation can be expressed as…”