Study on Coalbed Methane Well Production Forecasting Based on VMD and Hybrid Time Series Models
Year: 2025
Type: article
Abstract: 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.
DOI: 10.54097/90ar4c70
Author: Qianyu Zheng
Institution: Henan Polytechnic University
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407440960