Optimizing Minimum Miscibility Pressure Prediction Using Machine Learning: A Comprehensive Evaluation and Validation

This study provides the proof-of-concept for identifying the most suitable machine-learning (ML) model that predicts minimum miscibility pressure (MMP) based on temperature, crude oil, and injected fluid composition. MMP defined as the lowest pressure injected gas developing miscibility with reservoir oil is crucial for gas-enhanced oil recovery. Slimtube experiments considered the most reliable for MMP predictions are time-consuming. Although researchers have considered ML to expedite MMP predictions, validation of the optimal model that integrates the main controlling factors remains outstanding. We tested eight ML models of different complexities to determine the most suitable for predicting MMP. The models were trained and tested using 75 and 25% of 142 publicly available slim-tube experiments and validated using six in-house slim-tube MMP experiments. The injected gas compositions varied and included H2S, CO2, N2, CH4, and C2 +. We assessed model suitability using mean absolute error (MAE). Models with MAEs