Naturally fractured carbonate reservoirs (NFR) host 50% of the world hydrocarbon reserves. Carbonate reservoirs are known for their high degree of heterogeneity and uncertainty in reservoir description. Characterizing the fracture system with reduced uncertainty helps in building predictable reservoir models that in turn are used by reservoir management for business decisions.

Integration of multi-data sources (static and dynamic) is vital to the understanding of the mechanisms of fluid flow present in a given reservoir. Calibration of geologic-based models (conditioned by static data) to flow-related data (well test and production data) can dramatically reduce the uncertainty in reservoir models.

In this work, we present a new development to further reduce the uncertainty in the characterization of fracture properties (e.g., orientation, conductivity, aperture, length and density) from well test pressure responses (e.g., permeability-thickness product, storativity, and interporosity). The optimization problem is addressed using a direct search method. A novel multi-level genetic algorithm is developed to find the optimum solution space of the fracture properties by minimizing the error in a new multi-objective function.

The proposed algorithm was benchmarked against the industrial software FracaFlow©. Synthetic data of heterogeneous systems were used for validation as well as to demonstrate the new algorithm capabilities. Our results clearly show further reduction of uncertainties in fracture property estimation compared to FracaFlow©.

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