Improving Drilling Rate Prediction with Advanced Data Preprocessing and LSTM-Attention Mechanisms for Offshore Oil Drilling

This study proposes a deep learning model combining Long Short-Term Memory (LSTM) and attention mechanisms for predicting drilling rate (ROP) in offshore oil drilling. The model processes time-series production data, extracts key features, and predicts future ROP with improved accuracy and real-time performance. Data preprocessing techniques, including outlier detection and duplicate removal, ensure high-quality data. The model outperforms traditional approaches such as LSTM, Temporal Convolutional Networks (TCN), and Gated Recurrent Units (GRU), achieving an RMSE of 0.154, MAE of 0.045, and an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.908. This LSTM-Attention model offers strong support for real-time drilling optimization and can be further enhanced by integrating domain-specific knowledge and real-time data updates.