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Abstract

Microporosity quantification is becoming increasingly important to assess the distribution of hydrocarbons and their remaining/residual saturations after water flood (and /or gas flood). Assessing uncertainties and limitations in microporosity estimations of carbonate cores, comprising different reservoir rock types have been a challenge for geoscientists. The advent of Digital Rock Physics (DRP) based measurements allow the pore 3D network images from micro and nano - Computed Tomography (CT) scans on selected sub-samples to map representative cores and Reservoir Rock Types (RRT). The DRP based microporosity is rigorously examined and compared with other techniques/tests. In Part I (Al-Ratrout, Kalam, Gomes, & Jouini, 2013) we presented conventional techniques, such as Mercury Injection Capillary Pressure (MICP), Nuclear Magnetic Resonance (NMR), Thin Section (TS) and Backscattered Scanning Electron Microscopy (BSEM) are used for semi-quantitative evaluations of microporosity. Images at different magnitudes (4X, 10X, 40X and 100X) were captured from TS and BSEM, and used to quantify porosity using image analysis software. NMR and MICP measurements acquired through a commercial laboratory were also analyzed to quantify the microporosity. DRP based 3D pore network images have been acquired at different scales of interrogation from nano to micro meters to define microporosity. In this paper we examine DRP results based on three state-ofthe- art techniques, such as Pore-Network Fusion, to combine micro and nano-CT to enhance microporosity estimations. Cutting edge nano level investigation involving Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and last technique is the 2D Large Mosaic Image using modular automated processing system (Maps). This work has shown DRP to be as excellent tool to assess microporosity, and quantify the microporosity effectively in 3D pore network. The evaluation with conventional techniques demonstrated the current industry limitations and uncertainty.

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/content/papers/10.3997/2214-4609-pdb.395.IPTC-17702-MS
2014-01-19
2020-06-05
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.395.IPTC-17702-MS
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