1887

Abstract

Summary

The automatization is a modern trend in various field of geology. In this work we present a system which were constructed based on convolutional neural network (CNN) for automated core description. The system was successfully applied to production data. The application of the system speeds up the core description process in 7x. A sedimetnologist spent 40 minutes to describe 60 meters of core in a scale of 1:10cm instead of 5 hours. The results are stored in digital format which removes all paperwork. The system helps to describe most of required lithologic types (rock type and its structure). In case of missed rare lithotype – user can add it to the system. A pipeline to prepare and train the CNN model described.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202156016
2021-08-04
2024-04-26
Loading full text...

Full text loading...

References

  1. Abashkin, V., Seleznev, I., Chertova, A., Samokhvalov, A., Istomin, S., and Romanov, D.
    [2020]. Digital analysis of the whole core photos.1st EAGE Digitalization Conference and Exhibition, 2020(1), 1–5. https://doi.org/10.3997/2214-4609.202032058
    [Google Scholar]
  2. Alzubaidi, F., Mostaghimi, P., Swietojanski, P., Clark, S. R., and Armstrong, R. T.
    [2021]. Automated lithology classification from drill core images using convolutional neural networks.Journal of Petroleum Science and Engineering, 197, 107933. https://doi.org/10.1016/j.petrol.2020.107933
    [Google Scholar]
  3. Baraboshkin, E. E., Baraboshkin, E. Y., Ismailova, L. S., Orlov, D. M., Zhukovskaya, E. A., Kalmykov, G. A., Khotylev, O. V., and Koroteev, D. A.
    [2020]. Deep convolutions for in-depth automated rock typing.Computers & Geosciences, 135. https://doi.org/10.1016/j.cageo.2019.104330
    [Google Scholar]
  4. Baraboshkin, E. E., Ivchenko, A. V, Ismailova, L. S., Orlov, D. M., Baraboshkin, E. Y., and Koroteev, D. A.
    [2018]. Core photos lithological interpretation using neural networks.20th International Sedimentological Congress.
    [Google Scholar]
  5. Baraboshkin, E., Ismailova, L., Orlov, D., and Koroteev, D.
    [2019]. Machine Vision Methods in the Application for Core Image Segmentation.Progress’19, 1–5. https://doi.org/10.3997/2214-4609.201953061
    [Google Scholar]
  6. Baraboshkin, E., Orlov, D., and Koroteev, D.
    [2020]. Tools for Automated Rock Description.First EAGE Digitalization Conference and Exhibition, 1–5. https://doi.org/10.3997/2214-4609.202032061
    [Google Scholar]
  7. Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P.
    [2002]. SMOTE: Synthetic Minority Over-sampling Technique. In Journal of Artificial Intelligence Research (Vol. 16).
    [Google Scholar]
  8. Cogswell, M., Ahmed, F., Girshick, R., Zitnick, L., and Batra, D.
    [2015]. Reducing Overfitting in Deep Networks by Decorrelating Representations. Retrieved from http://arxiv.org/abs/1511.06068
    [Google Scholar]
  9. Goodfellow, I., Bengio, Y., and Courville, A.
    [2016]. Deep Learning. MIT Press.
    [Google Scholar]
  10. GOST R 53375-2016
    GOST R 53375-2016. Oil and gas wells. Geological-technological logging. General requirements. [2016].
    [Google Scholar]
  11. Hall, J., Ponzi, M., Gonfalini, M., and Maletti, G.
    [1996]. Automatic Extraction And Characterisation Of Geological Features And Textures From Borehole Images And Core Photographs.SPWLA 37th Annual Logging Symposium, 1–13. New Orlean, Louisiana.
    [Google Scholar]
  12. He, K., Zhang, X., Ren, S., and Sun, J.
    [2015]. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Retrieved from http://arxiv.org/abs/1502.01852
    [Google Scholar]
  13. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D.
    [1990]. Handwritten Digit Recognition with a Back-Propagation Network.Advances in Neural Information Processing Systems, (2), 396–404. Retrieved from http://papers.nips.cc/paper/293-handwritten-digit-recognition-with-a-back-propagation-network.pdf
    [Google Scholar]
  14. Leeder, M. R.
    [1982]. Sedimentology. Process and Product. London: George Allen & Unwin Ltd.
    [Google Scholar]
  15. Lepistö, L.
    [2005]. Rock image classification using color features in Gabor space.Journal of Electronic Imaging, 14(4), 040503. https://doi.org/10.1117/1.2149872
    [Google Scholar]
  16. Makienko, D., Seleznev, I., and Safonov, I.
    [2020]. The effect of the imbalanced training dataset on the quality of classification of lithotypes via whole core photos.
    [Google Scholar]
  17. Müller, R., Kornblith, S., Google, G. H., and Toronto, B.
    [n.d.]. When Does Label Smoothing Help?
    [Google Scholar]
  18. Thomas, A., Rider, M., Curtis, A., and MacArthur, A.
    [2011]. Automated lithology extraction from core photographs.First Break, 29(6), 103–109. Retrieved from https://www.geos.ed.ac.uk/homes/acurtis/Thomas_etal_FirstBreak_2011.pdf
    [Google Scholar]
  19. Wieling, I. S.
    [2013]. Facies and permeability prediction based on analysis of core images. Retrieved from https://repository.tudelft.nl/islandora/object/uuid%3A9b6bd4b0-1001-4d9b-a6eb-7761bc3b2309
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202156016
Loading
/content/papers/10.3997/2214-4609.202156016
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error