# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
## 3 Results and Discussion
### Construction of LSTM-Attention Model  
As illustrated in Figure 5, the LSTM-Attention model comprises three main components: the LSTM layer, the attention layer, and the dense layer. This model integrates the capabilities of temporal feature extraction and dynamic weight allocation to better address the complex data patterns associated with predicting drilling pump energy consumption. The following sections elaborate on the design principles and functionalities of each layer in the context of practical applications. The LSTM layer is designed to handle the complex temporal dependencies within the sequential data and extract dynamic features of energy consumption changes during drilling operations. The energy consumption of drilling pumps is influenced by multiple factors such as pump pressure, flow rate, drilling depth, mud density, and mechanical wear. These factors exhibit intricate dependencies over different time steps. In the input stage, drilling data are fed into the LSTM layer in the form of time sequences. Each LSTM unit utilizes its internal memory and gating mechanisms to effectively capture the dynamic changes and long-term dependencies among various parameters in the time series. The LSTM layer maps the input sequence to a series of hidden states, which encapsulate historical information across different time steps, providing a detailed feature representation for subsequent layers. For example, as drilling progresses through various geological formations, the pump's operating conditions may change significantly. The LSTM layer can identify and retain these temporal variation features, enabling accurate prediction of future energy consumption trends. The attention layer is introduced to address the varying contributions of different time steps and parameters to energy consumption. During drilling, operational conditions such as drilling depth and formation hardness can have differential impacts on pump energy consumption. The attention mechanism automatically allocates weights to each time step, allowing the model to focus on the key time periods that most significantly affect energy consumption. In the figure, each hidden-state output by the LSTM layer is assigned an attention weight through the attention mechanism. These weights are normalized using the SoftMax function, reflecting the relative importance of different time steps. This enables the attention layer to focus on time periods or specific operational parameters that have  
Figure 5: LSTM-Attention model. a greater impact on energy consumption variations, thereby extracting more meaningful contextual information. The context vector is generated by a weighted sum of the hidden states, representing the most important information throughout the sequence. For drilling pump energy consumption prediction, this mechanism helps the model capture critical features during operational changes, such as increased wear on the drill bit or variations in formation properties, and accurately predict their impact on energy consumption. The dense layer, also known as the fully connected layer, is responsible for mapping the contextual information extracted by the attention layer to specific energy consumption predictions. The context vector, which summarizes the key time steps of the sequence, serves as the core basis for energy consumption prediction. In the context of drilling pump energy consumption prediction, the model needs to consider the combined influence of multiple operational parameters on energy consumption. Therefore, the dense layer not only captures the direct impact of each parameter but also models the interactions between different parameters. For example, combined variations in pump pressure and flow rate can lead to nonlinear fluctuations in energy consumption. The dense layer can model and predict these complex relationships through nonlinear mappings. By transforming the high-dimensional features in the context vector into specific energy consumption values, the dense layer provides precise prediction outputs.