Drilling Rate Prediction Based on Bayesian Optimization LSTM Algorithm with Fusion Feature Selection

The Rate of Penetration (ROP), as a core indicator for evaluating drilling efficiency, holds significant importance for optimizing drilling parameter configurations, enhancing drilling efficiency, and reducing operational costs. To address the limitations of existing ROP prediction models—such as difficulties in modeling, solution complexity, and inefficient utilization of field big data—this paper proposes a Bayesian-Optimized LSTM-based ROP prediction model with fused feature selection (BO-LSTM-FS). The model innovatively introduces a sequential-cross-validation fused feature selection framework, which organically integrates Pearson correlation analysis, variance filtering, and mutual information, and incorporates a forward search strategy for final validation. Building on this, the Bayesian optimization algorithm is employed for systematic global optimization of the key hyperparameters of the LSTM neural network. Experimental results demonstrate that the BO-LSTM-FS model achieves significant performance improvements compared to traditional Backpropagation (BP) neural networks, standard LSTM neural networks, and CNN-LSTM models: Mean Absolute Error (MAE) is reduced by 48.0%, 29.3%, and 23.5%, respectively; Root Mean Square Error (RMSE) by 45.5%, 38.5%, and 32.2%, respectively; Mean Absolute Percentage Error (MAPE) by 47.8%, 29.4%, and 22.6%, respectively; and the Coefficient of Determination (R2) is increased by 8.6%, 4.4%, and 3.0%, respectively. The model exhibits high prediction accuracy, fast convergence speed, and strong generalization capability, providing a scientific reference for improving the Rate of Penetration in practical drilling operations.