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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-of-the-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. Introduction and Background Microporosity is becoming a key parameter to estimate the total porosity system in carbonate reservoirs, and its great impact is observed in many parameters, such as defining the accurate original distribution of hydrocarbons in place (STOOIP), and in the relationship between permeability and total porosity that determine the Reservoir Rock Types (RRT's) and / or Petrophysical Groups (PG's) (Beachle, Weger, Eberli, & Massaferro, 2004). The impact of microporosity on acoustic elastic properties of both shear-wave velocity (Vs) and compressional-wave or pull-push velocity (Vp) has been documented with the effect on the resistivity logs in reading the water saturation in reservoirs, known by "Low Resistivity Pay Zones" (Anselmetti & Eberli, 1997). Also microporosity has a major impact in the segmentation process of the primary stage in conducting Digital Rock Physics (DRP) calculations; it plays a big role in defining the grey level threshold that define the actual grain boundaries, which eventually lead to build the 3D pore network, representative of our reservoirs(Kalam, 2012).

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. 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 micron meters to define microporosity. Preliminary measurements on carbonate cores from a giant onshore field of Abu Dhabi, reveals an inverse relationship between microporosity and permeability.Microporosity impacts the porosity-permeability correlation. The relationship between porosity and permeability of carbonate reservoirs shows a better correlation when microporosity is deducted from the total porosity of the reservoir. A detailed analysis is presented along with Best Practices in estimating microporosity of carbonates.

Reservoir rock typing is a process by which geological facies are characterized by their dynamic behavior. The dynamic behavior of the facies is assessed by studying the rock texture, the diagenetic processes which overprinted the initial fabric, and the interaction between the rock itself and the fluids. Porosity, permeability and pore size distributions characterize the rock texture while capillary pressure, relative permeability and wettability describe the rock-fluid interaction. Reservoir rock typing is a synergetic process between geology and petrophysics/SCAL. It is therefore a process by which various petrophysical parameters and dynamic measurements obtained from SCAL are integrated in a consistent manner with geological facies (lithofacies) to estimate their flow (dynamic) behavior. The relationships between lithofacies and reservoir rock types (RRTs) is complex because of the inter-play between facies, diagenetic processes and the rock-fluid interaction (wettability changes) in the reservoir. Similar lithofacies, deposited under the same depositional environments, may exhibit different petrophysical properties due to diagenesis. Therefore, lithofacies deposited under similar geological conditions may experience different diagenetic processes resulting in different petrophysical groups with distinct porosity-permeability relationship, capillary pressure profile and water saturation (Sw) for a given height above the Free Water Level (FWL). On the contrary, lithofacies deposited in different depositional environments, might exhibit similar petrophysical properties and dynamic behavior. The authors emphasize on the need to have a good understanding of the original facies, depositional environments, subsequent diagenetic processes and rock-fluid interaction (via SCAL) to be able to unravel the relationships between lithofacies, petrophysical groups and rock types. A workflow for carbonate rock typing addressing some of the industry pitfalls and the differences between lithofacies, petrophysical groups and rock types are presented in this paper.