Meta-Learning Enhanced Random Forest Ensemble for Short-Term Oil Well Production Forecasting

Accurate short-term oil production forecasting is critical for national energy security and strategic resource planning. However, in practice, the prevalent reliance on extensive daily production histories for training sophisticated models are often impractical. Consequently, a significant challenge lies in developing stable and precise forecasting models that utilize inherently limited monthly production data. To address this limitation, a meta-learning architecture is proposed to enhance the temporal representational capacity of ensemble random forests (RFs) for short-term production prediction. Specifically, three innovative meta-ensemble frameworks are introduced and evaluated: (1) independent RF base learners integrated with RF meta-learners, (2) time-embedded sequential RF base learners integrated with RF meta-learners, and (3) time-embedded sequential RF base learners with a fully connected network (FCN) meta-learner. Rigorous validation on a substantial dataset comprising 2558 wells from the Daqing Oilfield, each with fewer than 70 months of available production data, demonstrates the superior efficacy of the meta-learning-enhanced ensembles. Experimental results show that these frameworks significantly outperform established production forecasting methods, including CNN-BiLSTM models that are recognized for their inherent tempo ral correlation capture. The proposed approach achieves a notable 22.3% reduction in Mean Absolute Error (MAE) and a 17.8% improvement in root mean squared error (RMSE) for three-month-ahead forecasts, underscoring its potential for robust production forecasting under data scarcity.