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One of the top concerns for carbonate reservoir evaluation is the effect of rock texture on permeability, capillary pressure and relative permeability. Recent advances in log analysis combined with new logging sensors that are sensitive to carbonate rock texture have led to an improved workflow for petrophysical analysis of carbonates. The authors have earlier described an approach to estimating permeability in carbonates from borehole NMR logs and electrical images, and have earlier studied the relationship between NMR T2 distributions and capillary pressure curves in carbonates. Additional enhancements have been made to this workflow to include estimates of relative permeability by modeling invasion of mud filtrate utilizing a fluid flow model in combination with array resistivity logs. Analyzing relative permeability in conjunction with formation permeability and capillary pressure leads to new insights in log-based rock typing for comparison with Special Core Analysis (SCAL) data. This workflow is presented in the form of a case study of a carbonate well in the UAE. The workflow will be reviewed in its entirety with particular emphasis on the relationship between log rock texture and permeability, capillary pressure and relative permeability. Introduction Many of the giant carbonate fields of the Middle East are undergoing Enhanced Oil Recovery (EOR) via water flood, gas injection and/or combinations of both to improve ultimate oil recovery. The advance of fluid fronts in fields undergoing EOR typically varies within the formation layers, with permeability usually being the controlling factor. Decades of experience with routine and special core analysis, surveillance logging and production logging have confirmed the critical role permeability plays in hydrocarbon recovery and Masalmeh et al. (2003) examine the effect relative permeability and capillary pressure play in EOR. Several authors, Gyllensten et al. (1999) and Haro (2004) have proposed various methods for estimating permeability from wireline logs and one of the most common is to identify rock types, which each have a particular porosity-permeability relationship. The workflow presented here facilitates various forms of rock typing. Marzouk et al. (1997) presented a form of carbonate rock typing based on porosity partitioning that is consistent with the Dunham Classification (Dunham, 1962, Embry and Klovan, 1971). Amaefule et al. (1993) describe a more generalized rock typing method based on the Flow Zone Indicator (FZI) that delineates rock types from poro-perm relationships. Grotsch et al. (1998) have presented a rock typing scheme based on property cut-offs, thin section analysis, and high pressure capillary pressure data and pore-throat size distributions. These three forms of rock typing can be supported by the workflow presented in this paper and, in addition to these methods, there is potential to examine a new form of rock typing based on estimates of relative permeability. Carbonate Analysis Workflow The workflow presented has three basic steps and is described in detail by Ramamoorthy et.al. (2008). At each step of this workflow there are log displays and cross-plots to check results against available core data. These checks will be described for each step. Both routine core analysis (RCAL) data such as porosity, permeability and grain density, and Special Core Analysis (SCAL) data such as mercury injection capillary pressure (MICP), and relative permeability curves can be utilized during the workflow. Even though such core data may be available only on a few key wells in a given field, the fine tuning of parameters based on the detailed core-log analysis can normally be applied on a field wide basis.

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Carbonate reservoirs often contain a complex mixture of pore sizes. In Bul Hanine field, Arab-DIII reservoir is almost entirely microporous throughout the field. Microporosity affects log responses and fluid flow properties. Proper identification and quantification of different porosity classes and their influence on the petrophysical parameters is crucial to accurately calculate hydrocarbon saturation. This paper presents the results of a multi-disciplinary workflow employed to identify and quantify the different porosity classes in the Arab-D reservoir.The workflow consists of core-and log-based analysis. The core-based analysis includes laser scanning confocal microscopy of thin sections from different reservoir facies, analysis of mercury injection data, and 3D pore network modeling. Confocal microscopy (0.25 micron resolution) quantified microporosity that cannot be seen or assessed through conventional petrography, while 3D pore network modeling helped evaluate the effect of the microporosity on the electrical parameters of the different reservoir facies. The log-based analysis includes analysis of Nuclear Magnetic Resonance logs (NMR) through spectral decomposition, interpretation of borehole images to evaluate the effects of diagenesis on the different reservoir facies, and other standard logs.Confocal microscopy demonstrated that pores smaller than 10 microns in diameter (micropores) in wackestone to packstone facies commonly comprise almost 100% of the total porosity. Burrowed, heterogeneous packstones and wackestones have 38 to 95% microporosity. Accurate quantification of microporosity from core using confocal microscopy permitted the computation of a continuous microporosity log using primarily NMR spectral decomposition and alternatively borehole images when NMR data is not available. After image to core calibration, rock fabric analysis using borehole images identified different bioturbation intensities with variable burrow sizes and varying burrow infill textures. Permeability enhancement can develop when burrowing architectures are well developed and filled with more permeable sediment, but diagenesis can also alter the porosity and permeability. The evaluation of electrical properties yielded insights into more effective rock property parameters, indicating that water saturation in these microporous networks may be lower than previously calculated. Pore network modeling showed that the microporosity fraction influences Archie's saturation exponent (ЉnЉ). By including a variable ЉnЉ value, weighted by the fraction of microporosity, water saturation computations can be reduced by 20%, therefore increasing volumetric and original oil in place.This workflow provides an innovative technique to characterize different porosity classes in heterogeneous carbonate reservoirs and quantify its impact on reservoir properties. It also provides a novel technique to calculate water saturation after correcting the effects of the microporosity presence in the different reservoir facies. This technique can be used...