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Abstract

Summary

With the development of technology in recent years, the amount of data that geoscientists have to work with is increasing in size and quantity. Conventional analytics methods are gradually supplemented by big-data analytics methods and tools, such as artificial intelligence applications, to improve work efficiency and productivity. To collect a large enough set of labels with high confidence to train and predict geological features (such as lithofacies) is time and economically consuming.

In this study, we propose an active learning workflow as a solution for the challenge mentioned above by showing an example of how we improve the quality, quantity and diversity of the lithofacies interpretation. An extensive dataset was used in this study, including: i) 700 000s cuttings samples, ii) 77 000 meters of core, and iii) 70 000s kilometers of logs from 1744 exploration wells in the Norwegian Continental Shelf.

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/content/papers/10.3997/2214-4609.202377017
2023-10-17
2025-04-29
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References

  1. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system.Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
    [Google Scholar]
  2. Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278–282). IEEE. 10.1109/ICDAR.1995.598994
    [Google Scholar]
  3. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234–241). Springer.
    [Google Scholar]
  4. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation.California Univ San Diego La Jolla Inst for Cognitive Science.
    [Google Scholar]
  5. Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning.Journal of big data, 6(1), 1–48. https://doi.org/10.1186/s40537-019-0197-0
    [Google Scholar]
  6. Tan, M., & Le, Q. (2021). EfficientNetV2: Smaller Models and Faster Training. Proceedings of Machine Learning Research, 139 (Proceedings of the 38th International Conference on Machine Learning), 10096–10106. Retrieved May 20, 2023. from http://proceedings.mlr.press/v139/tan21a.html
    [Google Scholar]
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