Optimizing Subsurface Characterization: A Flow Zone Indicator Framework for Improved Permeability Modeling

This study introduces a robust and comprehensive framework for characterizing and predicting permeability in heterogeneous reservoirs by leveraging the Flow Zone Indicator (FZI) concept. We demonstrate that while a single global porosity-permeability relationship provides a limited understanding (R2 = 0.6493), it fails to capture the intricate, multi-scale heterogeneity that governs fluid flow. Our methodology successfully delineates the reservoir into five distinct hydraulic flow units (FZI 0-4), each exhibiting a unique and highly predictive exponential relationship between porosity and permeability. The remarkably high coefficients of determination within these units (e.g., R2 = 0.9479 for FZI 0) provide compelling evidence that FZI effectively segregates the reservoir into truly homogeneous domains. This approach enables the development of unit-specific predictive models that are significantly more accurate than a single bulk-rock correlation. Furthermore, the validation of core porosity against log porosity allows for the generation of high-resolution, continuous permeability profiles across uncored intervals, which is crucial for comprehensive reservoir characterization. This work affirms that the FZI method is an indispensable tool for advanced reservoir petrophysics, moving beyond simplistic correlations to unlock a granular understanding of reservoir quality. The resulting precise permeability models are fundamental for optimizing well placement, improving fluid flow simulations, and designing more effective enhanced oil recovery (EOR) strategies. This methodology has broader implications for sustainable development, aligning with Sustainable Development Goals (SDG) 9: Industry, Innovation, and Infrastructure by enhancing resource efficiency and supporting cleaner energy applications like carbon capture and storage (CCUS) and geothermal energy.