Study on Coalbed Methane Well Production Forecasting Based on VMD and Hybrid Time Series Models

To address the challenges of non-stationarity and nonlinearity in forecasting daily gas production for coalbed methane (CBM) wells, a hybrid prediction model incorporating Variational Mode Decomposition (VMD), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), and Sparrow Search Algorithm-optimized Support Vector Regression (SSA-SVR) is proposed. Initially, VMD adaptively decomposes the original time series into a series of intrinsic mode functions, followed by assessing each mode’s complexity via permutation entropy for precise feature delineation. CNN-LSTM is then applied to capture deep spatiotemporal features of highly complex components, while SSA-SVR effectively predicts lower-complexity components through regression. Ultimately, the predictions are linearly combined to determine CBM wells' daily gas production. Comparative experiments reveal that the proposed model attains a mean absolute error (MAE) of 1.5158, root mean square error (RMSE) of 2.0608, and coefficient of determination (R²) of 0.9998, outperforming other commonly used models in prediction accuracy and generalization capacity. This framework presents an enhanced model structure for CBM daily production forecasting.