Accurate ROP (rate of penetration) prediction contributes to better production task planning, ensuring efficient production line operation, and reducing production costs. ROP prediction is influenced by multiple factors, making accurate prediction challenging. Current research primarily relies on historical data for training and modeling, lacking methods for real-time ROP prediction. This paper introduces a GRU-Informer model for real-time ROP prediction. The model employs GRU (Gated Recurrent Unit) neural networks at the lower level to capture short-term correlations in drilling parameters and uses the Informer model at the top to address long-term dependencies among drilling parameters. Thus, the GRU-Informer can capture both short-term and long-term time dependencies, providing better ROP predictions. This paper constructs a dataset using historical data from a southwestern Chinese oil field for experimentation. RMSE (Root Mean Square Error), MAE (mean absolute error) and $${R}^{2}$$ (Coefficient of Determination) are employed as evaluation metrics for the model. Experimental results demonstrate that the GRU-Informer outperforms traditional recurrent neural networks like LSTM (Long Short-Term Memory), GRU neural networks and Informer in real-time ROP prediction, indicating its practical value.