Temporal Convolutional and LSTM Networks for Complex Mechanical Drilling Speed Prediction

Abstract

Accurate prediction of drilling speed is essential in mechanical drilling operations, as it improves operational efficiency, enhances safety, and reduces overall costs. Traditional prediction methods, however, are often constrained by delayed responsiveness, limited exploitation of real-time parameters, and inadequate capability to model complex temporal dependencies, ultimately resulting in suboptimal performance. To overcome these limitations, this study introduces a novel model termed CTLSF (CNN-TCN-LSTM with Self-Attention), which integrates multiple neural network architectures within a symmetry-aware framework. The model achieves architectural symmetry through the coordinated interplay of spatial and temporal learning modules, each contributing complementary strengths to the prediction task. Specifically, Convolutional Neural Networks (CNNs) extract localized spatial features from sequential drilling data, while Temporal Convolutional Networks (TCNs) capture long-range temporal dependencies through dilated convolutions and residual connections. In parallel, Long Short-Term Memory (LSTM) networks model unidirectional temporal dynamics, and a self-attention mechanism adaptively highlights salient temporal patterns. Furthermore, a sliding window strategy is employed to enable real-time prediction on streaming data. Comprehensive experiments conducted on the Volve oilfield dataset demonstrate that the proposed CTLSF model substantially outperforms conventional data-driven approaches, achieving a low Mean Absolute Error (MAE) of 0.8439, a Mean Absolute Percentage Error (MAPE) of 2.19%, and a high coefficient of determination (R2) of 0.9831. These results highlight the effectiveness, robustness, and symmetry-aware design of the CTLSF model in predicting mechanical drilling speed under complex real-world conditions.