http://dx.doi.org/10.4172/2381-8719.1000233 Open Access Research Article Volume 5 • Issue 1 • 1000233 J o u r n a l o f G e olo g y & G e o s c i e n c e s ISSN: 2329-6755 Geology & Geophysics J Geol Geophys ISSN: 2381-8719 JGG, an open access journal *Corresponding author: Bell JD, Petroregos, Unit 378, 165 Cross Avenue, Oakville, Ontario, L6J 0A9 Canada, London South Bank University, Department of Chemical and Petroleum Engineering, London, UK, Tel: +905-4653517; E-mail: juliedeebell@gmail.com Received October 04, 2015; Accepted December 04, 2015; Published December 09, 2015 Citation: Bell JD, Eruteya OE (2015) Estimating Properties of Unconsolidated Petroleum Reservoirs using Image Analysis. J Geol Geophys 5: 233. doi:10.4172/2381-8719.1000233 Copyright: © 2015 Bell JD, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Keywords: Oil sands fabric; Oil sands petrology; Reservoir properties; Image analysis Introduction Reservoirs that are non-crystalline rock are common worldwide and include tight gas sands, heavy oil, and bitumen rich sands. Historically, these unconsolidated reservoirs have been treated as rock reservoirs and are, therefore, greatly misunderstood. Determining reservoir properties, such as porosity and permeability, has been over- reliant on well log-based methods and other macroscopic reservoir evaluation methods. Tese include modelling and numerical simulation from cores and core plugs which can be difcult in sand reservoirs. As these reservoirs are non-crystalline, the integrity of samples can be compromised during conventional analysis. Quantitative oil sands fabric analysis employs image analysis which quickly provides direct measurements of reservoir properties for sand reservoirs. Trough thin section images, reservoir porosity can be directly derived, especially within regions of interest (ROI). Te recent advances in computer aided 2D image analysis, as applied to thin sections prepared from reservoir core samples, now provide the tools for rapid characterization and estimation of reservoir properties [1]. Tese properties include porosity, permeability, hydraulic conductivity and grain size distribution, amongst others. Te method for using thin sections to determine reservoir properties in sand reservoirs, such as oil sands, is presented in the following section. Materials and Methods Te study was based upon ten mammoth-sized petrographic thin sections of argillaceous sand and bioturbated samples from the Upper McMurray Hangingstone River outcrop in Alberta, Canada. Te Hangingstone River outcrop is a well-documented estuarine environment [2] with bioturbation features reported by Pemberton et al. [3]. At the Hangingstone River outcrop, the Upper McMurray Member underlies the Wabiskaw Member. In general, the upper member of the McMurray Formation consists of horizontally bedded sands and muds; the sands are argillaceous and very fne grained, interpreted to be formed of bank or of channel from a brackish water setting [3]. Te mammoth-sized petrographic thin sections were prepared according to Jongerius and Heintzberger [4] and Fitzpatrick [5], although procedures were modifed in order to maintain sample integrity and minimize an alteration of the oil contained in the samples [6]. In a qualitative, or oil sand fabric descriptive analysis, reservoir constituents are grouped into coarse and fne components afer setting a size limit, commonly of 20 μm, which is the upper limit for silt. Reservoir constituents are grouped into components based on a coarse to fne c/f ratio. Coarse components are sand size and above; fne components include silt and clay; bitumen is classifed according to size and thus can be categorized either as a coarse or a fne component. Voids, which are spaces not occupied by solid material, are identifed according to shape and size. Te qualitative descriptive study carried out prior to image analysis revealed that the coarse components in the argillaceous sands were predominately quartz grains which ranged between very fne and medium sand according to the Wentworth scale, while the fne component was speckled and dark to very dark brown in colour [7]. Voids in the argillaceous sands were macropore in size and consisted of vughs with some complex packing voids (Figure 1). Te coarse components in the bioturbated samples were predominately quartz grains which ranged in size from fne to medium sand sized according to the Wentworth scale. Te fner component was speckled and brown to dark brown in colour. Voids were macropore in size and consisted predominately of vughs with a few complex packing voids (Figure Abstract Unconsolidated petroleum reservoirs are non-crystalline rock and include tight gas sands, heavy oil, and bitumen rich sands. These can be both near surface and at depth. This is a “methods” paper which discusses the use of thin section image analysis to obtain reservoir properties from unconsolidated sand reservoirs. A case study was carried out on samples from the Upper McMurray Member of the Athabasca Oil Sands in Canada. Using petrographic image analysis, porosity and permeability were obtained from thin sections from selected regions of interest (ROI). This was accomplished by generating 2D grain and void architecture models from binary images. Porosity and averaged particle size distribution for the coarse components or sand size components were derived directly from oil sand thin sections, whereas permeability was calculated. Values of porosity and permeability obtained using this method were found to be in typical value ranges of those obtained by conventional methods, including petrophysical and core analysis. The application of image analysis to sand reservoirs, such as oil sands, is a rapid and economical method of estimating reservoir properties, especially within regions of interest, and there is the added advantage of direct observation. Estimating Properties of Unconsolidated Petroleum Reservoirs using Image Analysis Bell JD 1,3 * and Eruteya OE 2,3 1 Petroregos, Unit 378, 165 Cross Avenue, Oakville, Ontario, L6J 0A9 Canada 2 University of Haifa, Department of Marine Geoscience, Haifa, Israel 3 London South Bank University, Department of Chemical and Petroleum Engineering, London, UK Bell and Eruteya, J Geol Geophys 2016, 5:1