# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
## 4 Conclusions  
This study proposed a novel LSTM-Attention model for accurately predicting drilling pump energy consumption. Through comprehensive experiments and comparisons with traditional LSTM and CNN models, the results demonstrated that the proposed model outperformed existing methods across multiple evaluation metrics. In the four test cases, the Mean Absolute Error (MAE) of the LSTM-Attention model ranged from 5.19 to 10.20, significantly lower than that of the traditional LSTM models (12.43 to 18.76) and CNN models (13.27 to 19.82). Additionally, the Mean Squared Error (MSE) values for the LSTM-Attention model, ranging from 45.02 to 66.42, were notably lower than those of the traditional LSTM models (130.67 to 180.91) and CNN models (140.85 to 190.74). The Root Mean Square Error (RMSE) ranged from 6.71 to 8.15, outperforming the traditional LSTM (11.43 to 13.45)  
Figure 11: Performance comparison with different indices: (**a**) MAE; (**b**) RMSE; (**c**) MSE; (**d**) R\({}^{2}\).  
and CNN models (12.78 to 14.52). The R\({}^{2}\) values were close to one (ranging from 0.95 to 0.98), indicating the model's exceptional performance in capturing the trends of energy consumption variations.  
The model's superior performance lies in its ability to handle both short-term fluctuations and long-term trends in energy consumption, which is crucial for optimizing drilling operations. Its robustness and adaptability under various operating conditions make it an ideal tool for real-time energy management and decision-making in drilling processes. The incorporation of the attention mechanism enhances the model's focus on critical features and time steps, further improving prediction accuracy and interpretability.  
Although the LSTM-Attention model shows significant improvements in prediction performance compared to traditional models, there is still room for further enhancement. Future research could consider incorporating additional operational parameters and external factors to refine the model's predictions under more diverse and challenging conditions. Moreover, exploring more advanced deep learning architectures, such as Transformer models or hybrid approaches, may offer even greater predictive power and applicability.  
Overall, the LSTM-Attention model provides a valuable framework for improving the accuracy and efficiency of drilling energy consumption forecasting. Its application can contribute to enhanced energy utilization, cost reduction, and improved operational safety, laying a solid foundation for intelligent and sustainable drilling practices in the oil and gas industry.  
Conceptualization, C.W.; Methodology, Z.Y.; Software, Z.Y.; Validation, C.W. and C.Z.; Formal analysis, C.W. and C.Z.; Investigation, Q.L.; Resources, Q.L. and Z.Z.; Data curation, Z.Y.; Writing--original draft, Z.Y.; Writing--review & editing, Z.Z.; Visualization, Q.L.; Supervision, C.Z.; Project administration, Z.Z. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support of the National Science Fund for Distinguished Young Scholars (No. 52125401).  
The datasets presented in this article are not readily available because of company confidentiality requirements. Requests to access the datasets should be directed to corresponding author.  
Author [PERSON] was employed by the Karamay Drilling Company of CNPC Western Drilling Engineering Co., Ltd. The authors declare that this study did not receive any funding from Karamay Drilling Company of CNPC Western Drilling Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.