Abstract
In the field of oil drilling, accurately predicting the Rate of Penetration (ROP) is crucial for improving drilling efficiency and reducing costs. Traditional prediction methods and existing machine learning approaches often lack accuracy and generalization capabilities, leading to suboptimal results in practical applications. This study proposes an end-to-end ROP prediction model based on BiLSTM-SA-IDBO, which integrates Bidirectional Long Short-Term Memory (BiLSTM), a Self-Attention mechanism (SA), and an Improved Dung Beetle Optimization algorithm (IDBO), incorporating the Bingham physical equation.We enhanced the DBO algorithm by using Sobol sequences for population initialization and integrating the Golden Sine algorithm and dynamic subtraction factors to develop a more robust IDBO. This optimized the BiLSTM-SA model, resulting in a BiLSTM-SA-IDBO model with an RMSE of 0.065, an R² of 0.963, and an MAE of 0.05 on the test set. Compared to the original BiLSTM-SA model, these metrics improved by 78%, 21%, and 83%, respectively. Additionally, we compared this model with BP Neural Network, Random Forest, XGBoost, and LSTM models, and found that our proposed model significantly outperformed these traditional models. Finally, through practical testing, the model's excellent predictive ability and generalization were verified, demonstrating its great potential for practical applications.