An Innovative Workflow for Predicting Residual Oil Saturation and Displacement Efficiencies for Subsurface Models from SCAL Data

Abstract This study addresses the overestimation of oil recovery in a reservoir under natural waterflood by refining residual oil saturation (Sorw) estimates using Special Core Analysis (SCAL) data. Historical dynamic models had predicted higher recoveries than actual well performance, prompting a reassessment of key input parameters. SCAL was performed on 10 core plugs, providing high-quality data such as porosity, permeability, contact angle, resistivity, and relative permeability curves. A new workflow was implemented to compute Rock Quality Index (RQI) for each plug and derive correlations for Sorw, Krwe, and Kroe. These were integrated into a revised dynamic model (Study-2), replacing the previous model's generic correlations (Study-1). The updated model, informed by SCAL-derived Sorw values, showed a 22% decrease in forecasted ultimate recovery, aligning more closely with observed well performance. Additionally, the SCAL-informed model better predicted key production behaviors such as water breakthrough and water cut trends. A blind test on a new well confirmed the model's reliability, with water cut predictions within 5% of actual values. This demonstrates that incorporating SCAL-based Sorw correlations significantly enhances the accuracy of recovery estimates and reservoir management decisions.