Abstract
Steam injection is an energy-intensive enhanced-oil-recovery method for high-viscosity reservoirs, and its performance is controlled by coupled heat delivery, steam condensation, temperature-dependent viscosity reduction, mobility change and reservoir filtration response. This study develops an experimentally validated physics-motivated residual-learning framework for forecasting oil production and selecting thermally rational steam-supply regimes. The model combines a physics-motivated semi-empirical baseline describing useful steam-related heat input, calibrated viscosity transformation, mobility growth, steam–oil ratio and a thermal-energy efficiency index with a residual-learning block fitted to measured regime-level records. The supervised forecasting task was performed at the regime level using 200 operating-regime records treated as the effective modelling units, with nested logs aggregated within regimes and within-group dependence examined through campaign-, reservoir-state- and well-availability-based checks. The 4800 steam-injection log entries and 4800 production-response log entries were treated as nested time-resolved measurements used only for regime-level aggregation, feature construction and quality-control checks; they were not counted as independent training samples. Blind-test validation produced R2 values of 0.974 for oil rate, 0.988 for cumulative oil production, 0.731 for steam–oil ratio and 0.828 for the thermal-energy efficiency index; the corresponding MAPE values were 4.56%, 3.86%, 4.48% and 3.29%, respectively. The error structure shows higher uncertainty for composite indicators than for direct production responses, which is consistent with the measurement chain. Response-surface and Pareto analyses identify bounded steam-supply operating regions where production gain remains balanced against specific steam consumption and the thermal-energy efficiency index.