PAI-Informer: A Production-Anchored Incremental Informer for Multistep Shale Gas Production Forecasting

Abstract Reliable multistep forecasting of shale gas production is hindered by nonstationary decline, operational disturbances, and error accumulation. This study develops a Production-Anchored Incremental Informer (PAI-Informer) for 1–7-day production forecasting. Instead of directly predicting absolute production, the model estimates log-production increments anchored to the latest observation and reconstructs the production trajectory through cumulative summation and inverse log transformation. A joint level-trend (JLT) loss constrains both production magnitude and adjacent-step variation. The model was evaluated using daily production and pressure data from three shale gas wells and compared with Persistence, CNN-LSTM, CNN-GRU-AM, Informer, Autoformer, and PatchTST across five random seeds. Across the three wells, with evaluation metrics calculated independently for each well and then averaged, PAI-Informer achieved mean values of R2 = 0.9071, RMSE = 0.6384 × 104 m3/d, MAE = 0.3838 × 104 m3/d, and sMAPE = 31.29%. Compared with Informer, it reduced RMSE, MAE, and sMAPE by 11.46%, 23.33%, and 30.03%, respectively, and ranked among the top two models in RMSE for every well and forecast horizon. Ablation experiments identified production-anchored incremental decoding as the principal source of improvement, while the joint loss enhanced prediction stability. These modifications introduced no additional trainable parameters and increased inference time by only 5.6%. SHAP analysis attributed 71.45% of the global importance to historical gas production. Leave-one-well-out testing revealed sensitivity to unseen high-rate regimes, highlighting the need for broader multiwell data and domain adaptation. PAI-Informer provides a lightweight and interpretable framework that reduces multistep forecasting drift through production-anchored incremental prediction and improves trajectory consistency through JLT optimization without increasing model complexity.