A Sensor-Aware Decoupled Learning Framework for Robust Multi-Scale Time-Series Forecasting in Oil Production Systems

Accurate forecasting of oil well production via field monitoring systems is significantly restricted by a structural conflict in modeling, where temporal dependency learning and nonlinear feature representation are closely coupled. Such coupling forces a trade-off between capturing long-term temporal dependencies and retaining sensitivity to short-term sensor fluctuations, while amplified local sensitivity easily increases noise interference and weakens model robustness under complex non-stationary sensor dynamics. To solve this problem, this study proposes a novel sensor-driven hybrid framework named Temporal Augmented Residual Network (TAR-Net), which adopts a decoupled paradigm to separate global temporal modeling and local fluctuation compensation explicitly. A multi-scale dilated Temporal Convolutional Network (TCN) extracts long-range temporal patterns from multi-source sensor data, and a LightGBM-based residual module conducts targeted error correction. Meanwhile, multi-scale temporal features and adaptive multi-fidelity Bayesian optimization are applied to enhance model adaptability. Validated on real sensor data from the Volve oilfield, TAR-Net surpasses 13 benchmark models with an R² of 0.9832 and a MAPE of 7.8%. Residual and trajectory analyses verify its balance between global trend consistency and local fluctuation sensitivity. This framework offers a robust sensor-aware solution for complex multi-scale temporal modeling in industrial production systems.