Fast prediction and sensitivity analysis of foamy oil production under controlled pressure decline using explainable machine learning

Heavy-oil reservoirs operating under solution-gas drive may exhibit foamy-oil flow behavior, in which dispersed gas and delayed gas mobility enhance oil recovery beyond conventional expectations. However, predicting foamy-oil production remains challenging because of complex multiphase transport processes and strong sensitivity to operational conditions, particularly pressure depletion rate. To address this challenge, this study develops a simulation-informed machine-learning surrogate framework for rapid and interpretable prediction of foamy-oil production under controlled pressure depletion conditions. A calibrated thermal–compositional model was constructed in CMG-STARS using laboratory depletion experiments conducted in a 2-m sand-pack system. A simulation-based design-of-experiments (DOE) approach was then employed to generate datasets spanning realistic ranges of fluid properties, relative permeability characteristics, and foamy-oil kinetic parameters. Gradient-boosting machine-learning models were trained to reproduce key production responses, including oil rate, gas rate, gas–oil ratio, and cumulative recovery. The resulting surrogate models achieved high predictive accuracy, with coefficients of determination exceeding 0.95 and average prediction errors below 5%, while reducing computational time by several orders of magnitude compared with full-physics simulations. Explainable machine-learning analysis was further applied to quantify the relative importance of governing parameters. The results indicate that pressure depletion rate is the dominant control on production behavior, followed by gas liberation kinetics and critical gas saturation. The proposed framework demonstrates how simulation-informed surrogate modeling combined with explainable machine learning can provide both rapid prediction capability and transparent sensitivity analysis for complex foamy-oil production systems. The workflow therefore enables efficient scenario evaluation and provides a practical decision-support tool for forecasting and optimizing foamy-oil production strategies.