Abstract
Drilling efficiency analysis typically relies on manual post-event analysis, which is subjective and arbitrary, failing to accurately reflect real-time field conditions in a timely manner. To enable real-time, accurate, and automatic identification of drilling conditions and improve drilling efficiency, the authors developed an intelligent identification model based on artificial neural networks. Using Pearson correlation coefficient for correlation analysis, eight drilling parameters from comprehensive logging data were selected as input for network training: well depth, bit position, hook height, hook load, weight on bit, rotary speed, torque, flow rate, and standpipe pressure. By comparing the performance of Long Short-Term Memory (LSTM) neural networks, BP, and CNN in real-time intelligent identification of drilling conditions in deep formations, it was found that LSTM outperformed the others. The LSTM model achieved a recognition accuracy of 97%, demonstrating its efficiency and reliability while providing important theoretical and technical support for effective drilling condition identification.
Data availability
The datasets generated and analysed during the present study are not publicly available because of applicable institutional guidelines but are available from the corresponding author upon reasonable request.
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Acknowledgements
We acknowledge the contribution of all authors on the whole MS.
Funding
Funding was supported by Research on Automated Crawler Core Drilling Equipment and Process Technology (E210100721).
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W.L. completed the writing, revision of the figures, conception, structure. Y.Y. completed the model building, analysisi, perform of the model, data analysis. D.F. completed the data analysis, model test, figures. Y.Z. completed the conception, language, data analysis, revision.
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Li, W., Yang, Y., Fan, D. et al. Intelligent identification method for drilling conditions based on artificial neural networks. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34242-6
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DOI: https://doi.org/10.1038/s41598-025-34242-6
