# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
## 3 Results and Discussion
### Experiments and Analysis
Despite the smoother fluctuations, the model still accurately tracks the changes, particularly during the 600-1200 s stable period, where the predicted values align almost perfectly with the actual data, highlighting the model's high prediction accuracy under  
Figure 10: Trends and fluctuations in Test 4.  
stable operating conditions. Figure 9c: This experiment spans a longer duration (0-2400 s) and includes several distinct phases of power changes. The model accurately captures these changes, especially during the power reduction phase between 1400-1800 s. The model also successfully predicts the rebound trend around 2000 s, showcasing its reliability in predicting long-term trends. Figure 10d: Covering a 2500-second interval, the pump power fluctuates significantly between 3750 kW and 4150 kW. The model provides precise predictions throughout this extended period, particularly during the intervals of 0-1000 s and 2000-2500 s, where significant power rises and falls are observed. This indicates the model's strong adaptability when dealing with long-term and wide-range fluctuations. **Effective Short-term Fluctuation Capture**. In all experiments, the actual data exhibit frequent short-term fluctuations, reflecting the dynamic changes in operational parameters such as pump pressure, flow rate, and drilling depth during the drilling process. The model is able to accurately predict the amplitude and frequency of these short-term fluctuations in most cases, indicating its excellent performance in short-term forecasting. Figure 7a: During the periods of 200-400 s and 800-1000 s, the pump power shows frequent rapid fluctuations. The predicted line almost perfectly matches these peaks and troughs, demonstrating the model's capacity to accurately capture rapid changes in pump power. This capability is crucial for identifying and anticipating sudden events or operational adjustments that impact energy consumption. Figure 8b: In the intervals of 600-800 s and 1200-1400 s, the pump power experiences relatively stable short-term fluctuations. The predicted values closely follow the actual data, indicating the model's ability to handle minor fluctuations in stable conditions effectively. This ensures stable prediction performance under calm operating conditions. Figure 9c: The model precisely predicts the short-term fluctuations during the periods of 1200-1400 s and 2000-2200 s. It accurately tracks the peaks and troughs, reflecting the model's quick response to rapid changes in power, which is beneficial for real-time monitoring and control. Figure 10d: During the periods of 500-700 s and 2000-2200 s, frequent high-frequency fluctuations are observed in the actual data. The model captures most of these fluctuations but shows slight lag during some abrupt changes around 2000 s. This suggests that while the model performs well in most high-frequency scenarios, its response speed and precision can still be improved in handling extremely rapid changes. **High-frequency Response and Analysis of Specific Operating Conditions**. While the model shows good robustness in handling high-frequency fluctuations and special operating conditions, it still exhibits some deviations during sudden severe fluctuations or abnormal conditions. Figure 7a: Around 1100 s, the actual data shows a sudden surge in pump power. The model's prediction lags slightly in this instance, indicating a delayed response to sudden power increases. This suggests that the model may require more detailed operational data, such as bit wear status and formation hardness, to improve its accuracy in such scenarios. Figure 8b: During the period of 1200-1600 s, the model successfully predicts multiple short-term increases and decreases in power, almost mirroring the actual values. This demonstrates the model's high prediction accuracy under steady and slow fluctuation conditions. Figure 9c: Around 2100 s, the actual pump power drops rapidly to 3850 kW.