A Hybrid Model Based on Attention Mechanism of TCN-BiLSTM for Oil Production Forecasting

Accurate oil production forecasting is crucial for supporting oilfield production planning. Traditional methods struggle to process multidimensional information effectively when faced with complex production data. They also exhibit insensitivity to temporal information in data sequences and perform poorly in long-term forecasting, which hinders comprehensive planning and decision-making. To address these challenges, a TCN-BiLSTM-Attention hybrid forecasting model is proposed in this study for long-term oil production forecasting. This model integrates a Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory network (BiLSTM), and an attention mechanism, capturing both local and global temporal dependencies while enabling the model to focus on critical features. Particle Swarm Optimization (PSO) is employed to tune hyperparameters, thereby further enhancing model efficiency and performance. Experimental results demonstrate that the proposed TCN-BiLSTM-Attention model outperforms traditional methods in long-term oil production forecasting. The optimized model achieves a coefficient of determination (R2) of 0.9903, exhibits higher efficiency in handling univariate and multivariate time series correlations, and demonstrates good robustness. Comparative experiments with various models confirm the significant advantages of this hybrid network architecture, providing a concrete model reference and case basis for the oil and gas industry.