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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.

Summary An experimental research program to investigate the effects of liquid saturations upon non-Darcy flow coefficients is presented. The presence of a wetting phase fluid plays an important role in high velocity flow of a gas well, producing condensate or water, and in propped fractures containing liquid saturations. This study initially examines the errors commonly encountered but ignored in evaluating the permeabilities and the coefficient of inertial resistance during the flow of gases through porous media. Experimental techniques, such as constant overburden pressure, changing overburden pressure, forward flow, and backpressure flow, are applied to optimize and obtain accurate evaluations of Klinkenberg parameters and inertial resistance coefficients for a selection of Omani reservoir cores. Gas-slippage factor significantly influences the derived viscous and inertial coefficients from highvelocity gas flow data. An increasing wetting phase saturation increases the non-Darcy coefficient up to thirty-fold. Analysis of the experimental data revealed that unique relationships exist between the non-Darcy flow coefficients and the equivalent liquid permeability, porosity, and liquid saturation. Heterogeneity of the core as mapped by pore-scale measurements provide an insight into the mechanism for such a large increase in the non-Darcy coefficients.

This paper presents laboratory measurements of the NMR properties of a petrographically and petrophysically heterogeneous group of reservoir rock samples (11 sandstones and 2 limestones) supplemented by data from the literature.5 For the main group of sandstones, cross-plots of T12 × φ4 and T1-stretched versus measured permeability indicate a broad correlation, although the spread is very large. With reference to our data, we attribute this spread to the combined influences on T1-stretched of (a) the presence of authigenic clays in varying quantities, (b) total iron content and (c) pore architecture. The latter is a complex function of detrital texture, degree of cementation, and the modes of occurrence and growth habits of the authigenic minerals — particularly with respect to the generation of micropores by the clays. The values of T1-stretched for 2 pure calcitic limestones and 3 quartzitic sandstone5 are markedly higher than those for the rest of the samples. Although the exact reasons are unclear, it is cautiously suggested that this may be due in part to calcite and quartz having lower surface relaxation strengths than authigenic clay.

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.

Artificial neural network (ANN) models are designed to emulate human information processing capabilities such as knowledge processing, speech, prediction and control. The ability of ANN systems to spontaneously learn from examples, reason over inexact and fuzzy data, and provide adequate responses to new information not previously seen, has generated increasing acceptance for this technology in the engineering field and resulted in numerous applications. A preliminary investigation into the use of this novel technology is presented towards predicting formation damage by quantifying wettability and two-phase relative permeability of oil reservoirs. An artificial neural network model based on the Back Propagation technique is trained with a number of variables from experimentally established relative permeability (relperm) curves. The reservoir core input data covers an extensive range of porosities and permeabilities from different lithologies having diverse wettabilities. The trained model is then tested with only a couple of easily obtainable input variables such as the Swc and Sor and predictions are made on the wettability and relperm curves. A change or shift in the relperm curves is associated with changes in wettability, and perhaps to formation damage in the drilling process. The wettabilities of the rock-fluid system are predicted to within 90 % of the experimentally determined values. The relperm curves, particularly the end-points are predicted to within 85 % of the measured results. The accuracy of the predictions are significantly enhanced with model training using more precise reservoir data and better defined formation lithologies. Neural networks have immense potential in predicting relperm curves and thereby assessing formation damage in reservoirs.