Published

2023-08-16

Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method

Técnicas de visión por computador aplicadas a la detección automática de sinusoides en imágenes resistivas de pozo – Una comparación con el método BMC

DOI:

https://doi.org/10.15446/esrj.v27n2.101556

Keywords:

Borehole resistivity imaging, autodip, mean square dip, computer vision, Hough’s transform, clustering (en)
Imágenes Resistivas de pozo, buzamiento automático, buzamiento medio cuadrático, visión por computador, transformada de Hough, agrupamiento (es)

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Authors

  • Jorge Alberto Leal Universidad Nacional de Colombia
  • Luis Hernan Ochoa Gutierrez Universidad Nacional de Colombia
  • Sergio Francisco Acosta Lenis Universidad Nacional de Colombia

In this research computer vision techniques are applied to borehole resistivity imaging in order to establish an alternative procedure to the mean square dip (MSD) processing. The MSD is regularly applied to detect sinusoids and dips automatically in borehole imaging and dipmeter logs. The present proposal is based on Gabor’s filters, morphological transformations, Hough’s transform, and clustering techniques. The MSD method and the computer vision proposal were tested in 1012 m of images, showing 7.986% of false positives for the MSD processing and 0.879% for the computer vision approach. This methodology tries to emulate the geologists behavior when they make image interpretation; instead of making correlations between resistivity curves like the MSD does. There are no special computer requirements, and it can be applied directly in the field for quick well-site dip results. This procedure can be easily integrated into log units and most commercial borehole-imaging processing software. The processing workflow was developed in python using standard libraries.

En esta investigación se aplicaron técnicas de visión por computador a imágenes resistivas de pozo para establecer un procedimiento alternativo al procesamiento del buzamiento medio cuadrático (BMC); BMC es regularmente empleado para detectar sinusoides y buzamientos automáticamente en registros de imágenes y de buzamiento. Esta propuesta se fundamenta en filtros Gabor, transformaciones morfológicas, transformada de Hough y técnicas de agrupación. El método BMC y la propuesta de visión por computador fueron probados en 1012 m de imágenes, mostrando 7.986% de falsos positivos para el procesamiento BMC y 0.879% para el enfoque de visión por computador. Esta metodología trata de emular el comportamiento de los geólogos cuando realizan interpretación de imágenes, en lugar de hacer correlaciones entre curvas de resistividad como hace el método BMC. No hay requisitos informáticos especiales y puede aplicarse directamente en campo para resultados rápidos de buzamientos. Esta metodología puede integrarse fácilmente a unidades de registro, así como también a la mayoría de programas de procesamiento de imágenes de pozo. Todos los procesos se desarrollaron en Python utilizando librerías estándares.

References

Al-Sit, W., Al-Nuaimy, W., Marelli, M., & Al-Ataby, A. (2015). Visual texture for automated characterisation of geological features in borehole televiewer imagery. Journal of Applied Geophysics, 119, 139-146. https://doi.org/10.1016/j.jappgeo.2015.05.015 DOI: https://doi.org/10.1016/j.jappgeo.2015.05.015

Arora, N., & Sarvani, G. (2017). A review paper on Gabor filter algorithm & its application. International Journal of Advanced Research In Electronics And Communication Engineering, 6 (9), 1003-1007.

Assous, S., Elkington, P., Clark, S., & Whetton, J. (2014). Automated detection of planar geological feature in borehole images. Society of Exploration Geophysicists Library, 79(1), 11-19. https://doi.org/10.1190/geo2013-0189.1 DOI: https://doi.org/10.1190/geo2013-0189.1

Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools. Dr. 120, 121-123.

Gabor, D. (1946). Theory of Communication. Journal of Institution of Electrical Engineers, 93(3), 429-457. DOI: https://doi.org/10.1049/ji-3-2.1946.0076

Grace, M., Newberry, B., Hansen, S., Berlitz, R., Cox, J., Davis, B., Fremgen, S. & Gilreath, J. (2000). Geologic applications of dipmeter and borehole images. Schlumberger Educational, Cambridge, The United States of America, 21pp.

Harris, C. R., Millman, K. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., . . . Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357-362. https://doi.org/10.1038/s41586-020-2649-2 DOI: https://doi.org/10.1038/s41586-020-2649-2

Khan, J. (2019). Guide to image inpainting: using machine learning to edit and correct defects in photos. https://heartbeat.fritz.ai/guide-to-image-inpainting-using-machine-learning-to-edit-and-correct-defects-in-photos-3c1b0e13bbd0 (last accessed June 2019).

Leal, J., Ochoa, L., & Sarmiento, G. (2022). Content of Total Organic Carbon Using Random Forest, Borehole Imaging, and Fractal Analysis: A Methodology Applied in the Cretaceous La Luna Formation, South America. Geofísica Internacional, 61(4), 301–323. DOI: https://doi.org/10.22201/igeof.00167169p.2022.61.4.2113

Leal, J., Ochoa, L., & Contreras, C. (2018). Automatic identification of calcareous lithologies using support vector machines, borehole logs and fractal dimension of borehole electrical imaging. Earth Sciences Research Journal, 22 (2), 75-82. https://doi.org/10.15446/esrj.v22n2.68320 DOI: https://doi.org/10.15446/esrj.v22n2.68320

Leal, J., Ochoa, L., & Garcia, J. (2016). Identification of natural fractures using resistive image logs, fractal dimension and support vector machines. Ingeniería e Investigación, 36 (3), 125-132. http://dx.doi.org/10.15446/ing.investig.v36n3.56198 DOI: https://doi.org/10.15446/ing.investig.v36n3.56198

Lindeberg, T. (1996). Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision, 30(2), 465-470. DOI: https://doi.org/10.1109/CVPR.1996.517113

Maynberg, O., & Kush, G. (2013). Airborne crown density estimation. International Society For Photogrammetry And Remote Sensing, 2(49), 49-54. DOI: https://doi.org/10.5194/isprsannals-II-3-W3-49-2013

McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, The United States of America, 56pp. DOI: https://doi.org/10.25080/Majora-92bf1922-00a

Negi, N. & Mathur, S. (2015). An improved method of edge detection based on Gabor wavelet transform. Recent Advances in Electrical Engineering and Electronic Devices, 1(2), 184-191.

Pratt, W. (2007). Digital image processing. Fourth edition. John Wiley and Sons, Los Altos, The United States of America, 420pp. DOI: https://doi.org/10.1002/0470097434

Ranjay, K. (2017). Computer vision: foundations and applications. Stanford University, Stanford, The United States of America, 18pp.

Rider, M. (2000). The geological interpretation of well logs second edition. French Consulting Ltd, Sutherland, United Kingdom, 173pp.

Shapiro, L., & Stockman, G. (2001). Computer vision. The University of Washington, Seattle, The United States of America, 107pp.

Tan, T., Stainbach M. & Kumar, V. (2006). Introduction to data mining. Pearson Addison-Wesley, Boston, The United States of America, 525pp.

Telea, A. (2004). An image inpainting technique based on the fast marching method. Journal of Graphic Tools, 9(1), 25-36. DOI: https://doi.org/10.1080/10867651.2004.10487596

Van der Walt, S., Schonberger, Johannes L, Nunez-Iglesias, J., Boulogne, Franccois, Warner, J. D. & Yager, N., Yu, T. (2014). Scikit-image: image processing in Python. PeerJ, 2(453), 2-18. DOI: https://doi.org/10.7717/peerj.453

How to Cite

APA

Leal, J. A., Ochoa Gutierrez, L. H. and Acosta Lenis, S. F. (2023). Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method. Earth Sciences Research Journal, 27(2), 139–147. https://doi.org/10.15446/esrj.v27n2.101556

ACM

[1]
Leal, J.A., Ochoa Gutierrez, L.H. and Acosta Lenis, S.F. 2023. Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method. Earth Sciences Research Journal. 27, 2 (Aug. 2023), 139–147. DOI:https://doi.org/10.15446/esrj.v27n2.101556.

ACS

(1)
Leal, J. A.; Ochoa Gutierrez, L. H.; Acosta Lenis, S. F. Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method. Earth sci. res. j. 2023, 27, 139-147.

ABNT

LEAL, J. A.; OCHOA GUTIERREZ, L. H.; ACOSTA LENIS, S. F. Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method. Earth Sciences Research Journal, [S. l.], v. 27, n. 2, p. 139–147, 2023. DOI: 10.15446/esrj.v27n2.101556. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/101556. Acesso em: 6 apr. 2025.

Chicago

Leal, Jorge Alberto, Luis Hernan Ochoa Gutierrez, and Sergio Francisco Acosta Lenis. 2023. “Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method”. Earth Sciences Research Journal 27 (2):139-47. https://doi.org/10.15446/esrj.v27n2.101556.

Harvard

Leal, J. A., Ochoa Gutierrez, L. H. and Acosta Lenis, S. F. (2023) “Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method”, Earth Sciences Research Journal, 27(2), pp. 139–147. doi: 10.15446/esrj.v27n2.101556.

IEEE

[1]
J. A. Leal, L. H. Ochoa Gutierrez, and S. F. Acosta Lenis, “Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method”, Earth sci. res. j., vol. 27, no. 2, pp. 139–147, Aug. 2023.

MLA

Leal, J. A., L. H. Ochoa Gutierrez, and S. F. Acosta Lenis. “Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method”. Earth Sciences Research Journal, vol. 27, no. 2, Aug. 2023, pp. 139-47, doi:10.15446/esrj.v27n2.101556.

Turabian

Leal, Jorge Alberto, Luis Hernan Ochoa Gutierrez, and Sergio Francisco Acosta Lenis. “Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method”. Earth Sciences Research Journal 27, no. 2 (August 16, 2023): 139–147. Accessed April 6, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/101556.

Vancouver

1.
Leal JA, Ochoa Gutierrez LH, Acosta Lenis SF. Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method. Earth sci. res. j. [Internet]. 2023 Aug. 16 [cited 2025 Apr. 6];27(2):139-47. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/101556

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