Proposing a Method Using Graph Attention Networks for Predicting Water Injection and Energy Consumption: A Case Study of a Surface Oil and Gas Gathering Station in Jilin Oilfield

In oilfield surface gathering and transportation systems, precise control of water injection is crucial for stabilizing wellhead pressure, ensuring efficient system operation, and conserving resources. This study proposes a water injection and energy consumption prediction model that integrates a graph attention mechanism. This model utilizes graph theory to combine the complex topology of oil and gas transportation networks with graph attention neural networks, capturing the complex relationships between nodes through attention mechanisms and enhancing feature representation capabilities. At the same time, by combining the gate control mechanism to further optimize feature selection, accurate prediction of water injection and energy consumption has been achieved. The results indicate that under a 3% error tolerance, the prediction accuracy for energy consumption at transfer stations is 100%, with water injection accuracy for individual nodes ranging from 80 to 90%. Compared to models such as MLP, random forest, XGBoost, SAGE, and GCN, the proposed GAT model demonstrates performance improvements of 9-65.6% in MAE, Huber loss, and RMSE metrics. Additionally, the study compares the predictive accuracy of various models under different signal-to-noise ratios, showing that the proposed model significantly outperforms others in terms of noise resistance, further validating its superior performance in prediction accuracy and robustness. Finally, a comparative analysis is conducted on the speed of model prediction and software computation. The results show that the software takes 17.65 s to complete the calculation, while the model prediction only takes 0.031 s, indicating that the constructed model has an advantage in efficiency. Based on all tests, this algorithm outperforms other models in terms of accuracy and noise resistance, meeting the practical requirements of field engineering.