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Improved Permeability Assessment Using Directional Rock Fabric Quantification


Quality and reliability of reservoir information is crucial in development of hydrocarbon resources, especially in heterogeneous and anisotropic formations such as carbonates. Reservoir characterization of carbonates is challenging due to the complex pore network, diagenetic overprints, and variation of petrophysical properties (e.g. conducting porosity, permeability) by depth and orientation. All of these factors need to be taken into account in evaluating formation data. Formation evaluation using routine core analysis and conventional well-log interpretation is insufficient to properly assess the petrophysical properties in carbonates. Combining multi-scale data by quantifying the pore network on pore-scale domain and applying it in core and log-scale domain will help improve the reliability of formation evaluation and eventually improve the quality of reservoir information needed in development strategies of the reservoir. The objectives of this paper include (a)quantifying the directional pore network connectivity of different rock types of a formation and investigating the correlation between the pore network connectivity with the geologically-defined rock fabric at different depths within the same formation, (b)evaluating the consistency of the estimated connectivity factor in core scale, and (c)developing a model to estimate permeability in the log-scale domain that incorporates the connectivity factor, In order to attain these goals, we extracted core samples from a carbonate formation at different depths. We scanned, processed, and digitized subsamples from each core and then obtained the three-dimensional directional pore network connectivity of the pore-scale images by simultaneously applying a streamline and a random walk algorithm. We developed a physics-based pore-scale directional connectivity model that is capable of representing the numerical results obtained in the pore-scale at different depths in the same formation. This model is then validated in the core-scale through laboratory measurements of electrical resistivity. The new quantitative measure of the rock fabric is finally used to incorporate the electrical resistivity logs for assessment of permeability. The directional pore network connectivity provided a persistent correlation between electrical resistivity and permeability at different scales, improving the estimation of permeability by 45% at different depths in the log-scale domain when compared to conventional methods.