基于 CNN-GRU-LightGBM 模型的单井产量 预测方法.

Accurately predicting daily production trends for individual wells in oilfield operations is crucial, but it poses a significant challenge due to the complex nature of oil well production conditions. A production model was developed based on multivariate time series data. Deep features were extracted using CNN-GRU (convolutional neural network-gate recurrent unit) for time series prediction, and predictions were also made using LightGBM ( light gradient boosting machine) framework from a regression perspective. To further enhance production prediction accuracy, the results of both approaches were integrated. Additionally, a method called the advanced parameter recursive prediction strategy was proposed, which allows for accurate production prediction even without known input features. This strategy involves forecasting important features that affect production in advance and applying these predicted features to simulate production prediction tests. The simulation results demonstrate that the model established in this study, combined with the advanced parameter recursive strategy, achieves the highest prediction accuracy on the test set. It significantly improves prediction accuracy compared to single-variable time series prediction and regression prediction models. [ABSTRACT FROM AUTHOR]