<![CDATA[The PSE Field, located in the Central Sumatra Basin, faces significant challenges due to outdated and incomplete fluid property data from Well X, where the last measurements were taken in 1992. This lack of comprehensive fluid data hampers accurate reservoir characterization, which is critical for optimizing production strategies. This study aims to bridge this gap by utilizing thermodynamic fluid characterization software (PVTp) to generate reliable fluid data, comparing two approaches: the Equation of State (EOS) model and the Black Oil model. Both models are evaluated based on key parameters such as saturation pressure (Psat), gas-oil ratio (GOR), (FVF), density, and viscosity. EOS model, grounded in thermodynamic principles, is compared to the empirically based Black Oil model to assess their predictive accuracy. The average absolute error percentage (AAE%) is used as a benchmark for performance. Results indicate that EOS model achieved an average AAE% of 1.2%, significantly lower than the 10.94% observed for the Black Oil model. Specifically, EOS model showed 0% error for Psat, 0.81% for relative volume, 3.7% for GOR, 1.4% for FVF, and 0.1% for density, while the Black Oil model demonstrated substantially higher errors, particularly for GOR (40.6%) and FVF (7.7%). This research highlights the limitations of the Black Oil model, especially in complex reservoirs where adjustments to laboratory data are necessary. In contrast, EOS model proves to be a more reliable alternative for accurate fluid characterization. The novelty of this study lies in its focus on the Central Sumatra Basin, where previous fluid property data was limited, making the validation of EOS model a valuable contribution to the field. The practical significance of this study extends beyond addressing the challenges of Well X, offering a framework that can be applied to other fields with similar data constraints. This research advocates for a transition from traditional Black Oil methods to more accurate EOS-based simulations, providing better decision-making tools for reservoir management and enabling greater efficiency and cost savings in future field operations]]>
