Hybrid Model Based on Attention Mechanism for Production Prediction of Sucker Rod Well

In oilfield production, the liquid production is an important indicator for measuring the production capacity of sucker rod wells and determining reasonable production parameters. Therefore, accurate metering of liquid production in sucker rod wells holds significant importance for oilfield automation production management. This paper proposed a physical-data hybrid-driven liquid production prediction method based on the attention mechanism to improve the accuracy of sucker rod well production metering. First, a physical-driven model for measuring liquid production based on the sucker rod well dynamometer cards is established, which ensures the rationality and interpretability of predicting liquid production. Then, a ResNet-based data-driven model is established to uncover the hidden features in downhole pump dynamometer cards and oil well production data. Finally, an attention mechanism is employed to couple the physical-driven and data-driven models, facilitating the identification of crucial features for liquid production prediction. The proposed method was tested on actual production data, and the average accuracy rate reached 95.67%, which was at least 2.43% higher than other best benchmark models for production prediction, and demonstrating good prediction accuracy and stability in special operating conditions. This approach successfully fuses the physical analytical model and data mining model of sucker rod wells, ultimately enhancing the interpretability and reliability of the model, thereby promoting efficient production management in oilfields.