Deep learning based early warning methodology for gas kick of deepwater drilling using pilot-scale rig data
Document Type
Article
Publication Date
4-1-2025
Abstract
Deepwater drilling operations demand highly accurate and efficient gas kick early warning systems due to their inherent complexity and associated risks. This study evaluates and optimizes three relatively advanced neural network models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU)—using a multi-source, long-duration time-series dataset derived from pilot-scale experiments. This dataset captures the intricate dynamics of gas migration along the downhole-riser-ground flow path under varied influx scenarios simulated by controlled air injection profiles. Each model was trained and tested to enhance performance across different early warning time windows. Comparative analysis demonstrated that the Bi-LSTM model exhibited the best overall accuracy in forecasting gas influx incidents, significantly extending the lead time for implementing effective well control measures. The findings underscore the efficacy of these models in enhancing drilling safety, optimizing operational efficiency, and supporting informed decision-making in complex offshore environments. Practical challenges and deployment strategies are also discussed to facilitate the application of these findings in real-world scenarios.
Publication Source (Journal or Book title)
Process Safety and Environmental Protection
Recommended Citation
Yin, Q., Zhu, Q., Song, Z., Guo, Y., Yang, J., Xu, Z., Chen, K., & Sun, L. (2025). Deep learning based early warning methodology for gas kick of deepwater drilling using pilot-scale rig data. Process Safety and Environmental Protection, 196 https://doi.org/10.1016/j.psep.2025.106844