Advanced Characterization of Carbonate Reservoirs: Integrating Production Interpretation and Dynamic Rock Typing for Enhanced Field Development

Abstract Carbonate reservoirs pose significant challenges for reservoir characterization due to their inherent geological complexity, heterogeneity, and intricate pore structures. These characteristics often lead to unpredictable fluid flow patterns and can result in suboptimal hydrocarbon recovery. Accurately characterizing such reservoirs is crucial for efficient resource extraction and optimizing production strategies. To address these challenges, this paper proposes an integrated approach that combines production data interpretation with dynamic rock typing, offering a more comprehensive understanding of carbonate reservoirs. The methodology involves the integration of production interpretation techniques with dynamic rock typing, utilizing data from well logs, core analysis, and high-resolution production records. This combination is enhanced by the application of history matching techniques, which align the reservoir model with observed historical production data. History matching serves as a critical step in validating the dynamic reservoir model, ensuring that the predictions made regarding fluid flow and reservoir performance are accurate and reliable. By associating rock types with their corresponding flow properties and fine-tuning the model based on historical production data, the approach allows for more precise predictions of reservoir behavior. This integrated approach proves highly effective in achieving a reliable history match, which is crucial for accurate reservoir characterization and fluid flow prediction. Improved modeling enhances field development strategies, boosts the overall recovery factor, and optimizes resource extraction in complex carbonate reservoirs. By refining reservoir engineering practices, this method contributes to increased production efficiency and more effective long-term management of hydrocarbon assets.