An Innovative Method for Coalbed Methane Well Production Prediction Based on Improved‐iTransformer Algorithm

ABSTRACT Accurate prediction of oil and gas field development indicators is critical to production optimization. Traditional machine learning suffers from limited capacity to capture complex nonlinear relationships, whereas conventional deep learning models face challenges including strong dependence on data quality and uneven prediction accuracy across the full production cycle. To address these issues, this article proposes an improved temporal attention fusion model—the Improved‐iTransformer. This model is designed with a dual‐time embedding enhancement module to adapt to multivariate coupled data, adopts a “dynamic window + controllable enhancement” strategy to process low‐quality data, and constructs a phased fine‐tuning mechanism to optimize the full‐cycle prediction effect. Experimental verification was conducted based on the daily production data of three coalbed methane wells (A‐1, A‐2, and A‐3) from a domestic production unit, with each well's data covering 2–4 years and including 9 key input features. Wells A‐2 and A‐3 were used to verify the generalization ability within the same coal seam, and additional cross‐coal‐seam and cross‐block experiments were conducted to further validate the model's transferability under different geological conditions. The mean absolute error (MAE), root mean square error (RMSE), and R 2 indicators from 10 independent training runs across the three wells were statistically analyzed, with box plots drawn to study the error distribution. Hyperparameter optimization was performed using grid search. After preprocessing steps including data cleaning, standardization, temporal reconstruction, and augmentation, comparative experiments were carried out with the classic long short‐term memory (LSTM), bidirectional long short‐term memory (Bi‐LSTM), standard transformer, and mainstream transformer derivative models Informer, Autoformer, and iTransformer. The results show that the Improved‐iTransformer performs excellently in fitting early production fluctuations and capturing late decline trends. Compared with the LSTM algorithm, its MAE is reduced by 26.08% and R 2 is increased by 0.16; compared with the standard transformer algorithm, the MAE is reduced by 42.28% and R 2 is increased by 0.22; the comprehensive prediction accuracy is also significantly higher than that of Informer, Autoformer, and the original iTransformer, with MAE reduced by 25.29% and R 2 increased by 0.07 relative to the original iTransformer, alongside a more uniform residual distribution. Out‐of‐sample forecasting validation on the three wells further confirms that the model has reliable generalization ability, effectively overcoming the defects of traditional models such as poor stage adaptability and high sensitivity to data quality, and providing a more reliable technical support for production prediction in unconventional oil and gas fields.