Research on Oilfield Development Data Prediction Based on EEMD-GRU Combination Model
Ru Liu, Jichao Qin, Qing He Hu, Chang Liu, Cheng Chen · 2025
Driven by computer intelligent algorithm, the price prediction method of raw material data required by the traditional oilfield development has been unable to meet the practical needs. In view of the problems of low prediction accuracy and poor time sequence, the paper uses the idea of combined model, and proposes an economic price prediction model that can effectively capture the data time series. It is found that when the input sequence length is 360 days and the neuron is 64, the study model achieves the best training results. The results of the model test show that the proportion of data loss affects the prediction accuracy, but under the proportion of various data loss, the prediction accuracy of the study model is better than that of the comparison model. When the proportion of data loss is 0.1, the Mean Absolute Error (MAE) of the study model is 2.22, the Root Mean Square Error (RMSE) is 2.94, and the Pearson Correlation Coefficient (PCC) is 0.95. The research model shows better generalization and robustness in the price prediction of oilfield development data, which can meet the needs of enterprises for the economic evaluation of oilfield development.