Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction

• URL: http://arxiv.org/abs/2508.05210v1
• Date: Thu, 07 Aug 2025 09:45:56 GMT
• Title: Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction
• Authors: Saddam Hussain Khan,
• Abstract summary: Rate of Penetration (ROP) is crucial for optimizing drilling operations.<n>Traditional empirical, physics-based, and basic machine learning models often fail to capture intricate temporal and contextual relationships.<n>We propose a novel hybrid deep learning architecture integrating Long Short-Term Memory (LSTM) networks, Transformer encoders, Time-Series Mixer (TS-Mixer) blocks.
• Score: 1.2432046687586285
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Rate of Penetration (ROP) is crucial for optimizing drilling operations; however, accurately predicting it is hindered by the complex, dynamic, and high-dimensional nature of drilling data. Traditional empirical, physics-based, and basic machine learning models often fail to capture intricate temporal and contextual relationships, resulting in suboptimal predictions and limited real-time utility. To address this gap, we propose a novel hybrid deep learning architecture integrating Long Short-Term Memory (LSTM) networks, Transformer encoders, Time-Series Mixer (TS-Mixer) blocks, and attention mechanisms to synergistically model temporal dependencies, static feature interactions, global context, and dynamic feature importance. Evaluated on a real-world drilling dataset, our model outperformed benchmarks (standalone LSTM, TS-Mixer, and simpler hybrids) with an R-squared score of 0.9988 and a Mean Absolute Percentage Error of 1.447%, as measured by standard regression metrics (R-squared, MAE, RMSE, MAPE). Model interpretability was ensured using SHAP and LIME, while actual vs. predicted curves and bias checks confirmed accuracy and fairness across scenarios. This advanced hybrid approach enables reliable real-time ROP prediction, paving the way for intelligent, cost-effective drilling optimization systems with significant operational impact.

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