Multi-Length Prediction of the Drilling Rate of Penetration Based on TCN–Informer

The Rate of Penetration (ROP) during drilling is nonstationary and exhibits coupled local fluctuations, which makes it challenging to model for accurate prediction. To address the challenge of modeling multi-scale temporal dependencies in drilling, this study introduces a hybrid TCN–Informer framework. It integrates the causal dilated Temporal Convolutional Network (TCN) for capturing short-term patterns with the Informer’s ProbSparse attention mechanism for modeling long-range dependencies. A comprehensive methodology is adopted, which includes a four-stage data preprocessing pipeline featuring per-well z-score standardization and label concatenation, a sliding-window training scheme to address cold-start issues, and an Optuna-based Bayesian search for hyperparameter optimization. The prediction performance of the models was evaluated across various input sequence lengths using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The results show that the proposed TCN–Informer demonstrates superior performance compared to Informer, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. Furthermore, the predictions of the TCN–Informer respond more rapidly to abrupt changes in the ROP and yield smoother, more stable results during intervals of stable ROP, validating its effectiveness in capturing both local and global temporal patterns.