Construction of an Extended-Reach Well Torque Prediction Model Based on Multi-Source Drilling Data Fusion and Analysis of Dominant Factors

Extended-reach wells contain long, highly inclined intervals in which drill-string/wellbore contact and cumulative friction evolve continuously with depth, making torque prediction strongly nonstationary. This study develops a multi-source torque-prediction framework using 5977 field samples from a deepwater extended-reach well in the East China Sea. To eliminate the optimistic bias caused by randomly mixing adjacent depth samples, all records were ordered by measured depth; preprocessing and feature screening were fitted only on historical data; hyperparameters were selected with forward-chaining TimeSeriesSplit; and model performance was assessed using both a fixed deep-depth stress test and expanding-window rolling-origin prediction. Training-only correlation screening and variance inflation factor analysis reduced multicollinearity before comparing ExtraTrees, XGBoost, LightGBM, BPNN, and time-aware ensemble regression. Static extrapolation to the deepest 15% of the well revealed severe distribution shift and poor long-range generalization. In contrast, 50 m rolling-forward updating produced reliable prediction: ExtraTrees achieved an MAE of 1.121, RMSE of 1.643, MAPE of 2.750%, R2 of 0.907, and Pearson correlation of 0.964, while LightGBM achieved the lowest MAE of 1.090. ExtraTrees R2 decreased from 0.927 at a 25 m horizon to 0.711 at 200 m, demonstrating progressive concept drift with prediction distance. SHAP analysis identified measured depth as the dominant predictive proxy, whereas WOB, flow rate, and RPM were more relevant to operational intervention. The revised framework therefore emphasizes leakage-free validation, adaptive updating, and separation of predictive from controllable factors rather than random-split interpolation accuracy.