1887

Abstract

Summary

The occurrence of geomechanical events (e.g. drag, stall, kick) might hinder production and incur in losses. In this context, manual classification of daily drilling reports is a daunting task. In this work, we propose a method -grounded in the In-Context Learning paradigm - that leverages a commercial Large Language Model to classify daily drilling reports so that we enrich the prompt fed to the LLM to boost its performance. Experimental results attest the effectiveness of our approach comapred to other three variants. This work might help the oil industry in extracting valuable information from large amount of data in order to mitigate losses and to support data driven decision making.

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/content/papers/10.3997/2214-4609.202539018
2025-03-24
2026-02-13
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References

  1. Carpenter, C. [2024] AI-BasedSystem Automates Textual Classification of Daily Drilling Reports. Journal of Petroleum Technology, 76(02), 55–57.
    [Google Scholar]
  2. Cinelli, L.P., de Oliveira, J.F., de Pinho, V.M., Passos, W.L., Padilla, R., Braz, P.F., Galves, B., Dalvi, D.P., Lewenfus, G., Ferreira, J.O., Ji, A.Y., de Oliveira, F.L., Gonçalves, C.J., Netto, S.L., da Silva, E.A. and de Campos, M.L. [2021] Automatic event identification and extraction from daily drilling reports using an expert system and artificial intelligence. Journal of Petroleum Science and Engineering, 205, 108939.
    [Google Scholar]
  3. Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., Sun, X., Li, L. and Sui, Z. [2024] A Survey on In-context Learning. arXiv preprint arXiv:2301.00234.
    [Google Scholar]
  4. van der Maaten, L. and Hinton, G. [2008] Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
    [Google Scholar]
  5. Mikolov, T., Chen, K., Corrado, G. and Dean, J. [2013] Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
    [Google Scholar]
  6. OpenAI [2024] GPT-4 Technical Report. arXiv preprint arXiv:2303.08774.
    [Google Scholar]
  7. Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J.Y. and Wen, J.R. [2023] A Survey of Large Language Models. arXiv preprint arXiv:2303.18223.
    [Google Scholar]
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