Maridiyah Adebukola Madehin

Accurate oil-rate forecasting is essential for effective reservoir management, yet it is challenged by nonlinear flow behavior, operational variability, and data uncertainty. Conventional physics-based approaches, such as decline-curve analysis, provide interpretability but lack short-term responsiveness, while purely data-driven machine-learning models, although accurate, may violate physical production trends. This study addresses this gap by developing a physics-guided hybrid forecasting framework. A dataset containing 22 engineered features representing pressure dynamics, temperature behavior, choke performance, production characteristics, and temporal attributes was used to train Linear Regression, Random Forest, Gradient Boosting, and Support Vector Regression models. An Arps decline-curve model was fitted to capture long-term reservoir behavior, and a hybrid model was constructed by anchoring machine-learning predictions to physics-based decline trends. Model performance was evaluated using MAE, RMSE, R2, and cross-validation. Results indicate that ensemble machine-learning models outperformed linear and kernel-based methods, with Gradient Boosting achieving an RMSE of 54.15 STB/day and an R2 of 0.9984. The proposed hybrid model further improved predictive accuracy, reducing RMSE to 53.83 STB/day and increasing R2 to 0.9986, demonstrating enhanced stability and physical consistency. The study concludes that integrating decline-curve physics with machine learning yields more reliable oil-rate forecasts. Future work should extend the framework to field-scale applications and incorporate uncertainty quantification to support production-optimization decisions.