An oil production prediction approach based on variational mode decomposition and ensemble learning model

Well production forecasting can provide scientific guidance for oilfield production and management, which is an indispensable part of the oilfield development process. In this study, the daily oil production data from oil wells are first decomposed into components with different frequencies by variational mode decomposition (VMD), which is usually used to process complex time series. The new features obtained from decomposition and other filtered features are then used as input data and for training and forecasting of GRU, TCN and Transformer models respectively. In the end, the three models are integrated as base learners using the Blending method, which specifically involves using the predicted outputs of the three models as new inputs to the RBFNN for training and realizing the final predictions. The VMD-Blending model was compared with traditional models based on the production dynamics data of three production wells in an oil field in the Tarim area, China. The result shows that VMD can effectively improve the prediction effect of the base learners, and the prediction effect of these models is further improved after Blending integration, and all of their prediction indexes are significantly better than those of the base learners and the traditional SVM and RNN models. The proposed VMD-Blending model has a well performance in the task of well capacity prediction and is an accurate and effective method for oil production prediction.