<![CDATA[Optimizing the Alkaline-Surfactant-Polymer (ASP) injection process remains a persistent challenge in Enhanced Oil Recovery (EOR), particularly in heterogeneous sandstone reservoirs where traditional reservoir simulators are constrained by high computational demands and limited flexibility. This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. Unlike previous approaches, our method blends high-fidelity CMOST simulation data with machine learning precision in which it enables real-time optimization with field-scale relevance. Using 500 simulation scenarios validated by laboratory input, the SL model achieved exceptional predictive performance (R² = 0.988, RMSE = 0.304), outperforming all individual learners. The optimal recovery factor (RF) of 79.49% was obtained with the finely tuned concentrations of surfactant (5483.29 ppm), polymer (2242.61 ppm), SO?²? (5610.15 ppm), CO?²? (7053.59 ppm), and Na? (9939.35 ppm). Remarkably, the SL approach could reduce optimization time from 10 hours (CMOST) to under 1 minute; this underscored its potential for real-time operational deployment. The novelty of this work lies in its integrated use of ensemble learning to capture the complex and non-linear interactions between ionic chemistry and oil mobilization behavior, offering a field-ready AI framework for rapid and adaptive EOR design. This approach paves the way for the intelligent optimization of ASP schemes by minimizing the reliance on computationally intensive simulations while ensuring chemical and economic efficiency in marginal or complex reservoirs.]]>
