Optimization of Oil Well Production Prediction Model Based on Inter-Attention and BiLSTM

Accurate prediction of future oil production is critical for decision-making in oil well operations. However, existing prediction models often lack precision due to the vast and complex nature of oil well data. This study proposes an oil well production prediction model based on the Inter-Attention Mechanism (IAM) and Bidirectional Long Short-Term Memory Network (BiLSTM), optimized using a Comprehensive Search Algorithm (CSA). By incorporating the Inter-Attention Mechanism, the model enhances its capacity to model complex time-series data. The CSA, combined with Sequential Quadratic Programming (SQP) and Monotone Basin Hopping (MBH) algorithms, ensures both global and local parameter optimization. Using historical data from an oil well in Sichuan, the feasibility of the proposed model was validated, demonstrating superior accuracy and robustness compared to other prediction models and optimization algorithms.