# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method  
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1 Karamay Drilling Company of CNPC Western Drilling Engineering Co., Ltd., Karamay 834000, China; [EMAIL_ADDRESS]  
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2 State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China; [EMAIL_ADDRESS] 2 [EMAIL_ADDRESS] 2 [EMAIL_ADDRESS] 2 zhangck@cup. In recent years, numerous scholars have proposed various methods for predicting energy consumption [5], with common approaches for drilling pump energy consumption prediction categorized into empirical models, statistical methods, and machine learning techniques. Empirical models primarily rely on empirical data and traditional mathematical models. These methods [6] typically calculate energy consumption based on the operational parameters of the drilling pump (such as pressure, flow rate, and rotational speed) and the physical properties of the drilling mud. While these methods are straightforward and intuitive, the empirical formulas are often based on particular data from particular facilities under particular conditions. Consequently, the accuracy of predictions can be compromised in real-world operations due to factors such as complex geological conditions and equipment aging. Additionally, empirical models [7] struggle to meet the demands of energy consumption prediction under complex operating conditions, particularly when dealing with multivariable and highly nonlinear relationships, resulting in a significant decline in prediction accuracy. Statistical methods involve analyzing historical data and building models to forecast future energy consumption [8], with common approaches including Autoregressive Integrated Moving Average (ARIMA) models and Gray Prediction Models (GMs). An ARIMA model employs a combination of autoregressive and moving average analyses on time series data to establish a mathematical framework for predicting future trends. In contrast [9], a Gray Prediction Model is suitable for small samples of and utilizes Gray System Theory to process time series data and forecast future energy consumption changes. While these methods perform well in capturing linear relationships and short-term predictions, their effectiveness diminishes in addressing the complexities and nonlinear characteristics of the oil drilling process. Furthermore, statistical methods are sensitive to outliers and noise, which can distort the prediction models and negatively impact the accuracy of results. Machine learning prediction methods primarily include Random Forests (RFs), Support Vector Machines (SVMs) [10], and Gradient Boosting Trees (GBTs). Random Forests improve model generalization by constructing numerous decision trees based on the principles of ensemble learning, allowing for the processing of extensive feature data. Support Vector Machines find optimal separating hyperplanes to accomplish classification and regression tasks in high-dimensional spaces. Gradient Boosting Trees build a series of weak learners incrementally [11], combining their predictions to achieve efficient and accurate energy consumption forecasting. These machine learning methods are particularly effective in handling complex nonlinear relationships, accommodating more intricate patterns and interactions in drilling pump energy consumption predictions. However, these methods face certain challenges. First, machine learning models often require extensive parameter tuning, which can lead to lower computational efficiency and a complex, time-consuming training process, making it difficult to meet the real-time requirements of prediction. Second, these methods are prone to overfitting when processing large-scale data, resulting in models that perform well on training data but poorly on unseen data. Additionally, because machine learning techniques depend on large datasets for training, their predictive accuracy can be significantly affected when data are scarce or of low quality.