Revista UIS Ingenierías

Vol. 21 No. 1 (2022): Revista UIS Ingenierías
Articles

Characterization of elastic properties in a sandstone rock sample using digital rock physics

PDF (Español (España))
  • Smelinyer Dariam Rivero-Méndez ,
  • Juan David Ordoñez-Martínez ,
  • Carlos Sebastián Carlos Sebastián Correa- Díaz,
  • Hernán Darío Mantilla-Hernández ,
  • Octavio Andrés González-Estrada
Smelinyer Dariam Rivero-Méndez
Universidad Industrial de Santander
Juan David Ordoñez-Martínez
Universidad Industrial de Santander
Carlos Sebastián Carlos Sebastián Correa- Díaz
Ecopetrol – Centro de Innovación y Tecnología ICP
Hernán Darío Mantilla-Hernández
Ecopetrol – Centro de Innovación y Tecnología ICP
Octavio Andrés González-Estrada
Universidad Industrial de Santander
DOI: https://doi.org/10.18273/revuin.v21n1-2022016

Published 2022-02-11

Keywords

  • finite element method,
  • digital rock physics,
  • elastic properties,
  • sandstone

How to Cite

Rivero-Méndez , S. D., Ordoñez-Martínez , J. D. ., Carlos Sebastián Correa- Díaz, C. S. ., Mantilla-Hernández , H. D. ., & González-Estrada, O. A. (2022). Characterization of elastic properties in a sandstone rock sample using digital rock physics. Revista UIS Ingenierías, 21(1), 211–222. https://doi.org/10.18273/revuin.v21n1-2022016

Copyright (c) 2022 Revista UIS Ingenierías

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Abstract

A methodology based on digital rock physics is proposed for a group of tomographic images taken from a core of sandstone extracted from an oil well, considering an anisotropic model of the material during the segmentation process. The rock sample, provided by the Colombian Petroleum Institute, is composed mainly of minerals such as quartz and calcite. First, a three-dimensional model is generated from the tomographic images. Then, a finite element mesh is created considering a material model that relates density and elastic modulus with the Hounsfield scale. Finally, a parametric study of the numerical model is performed and the results are compared with the reference values. Three different tests are proposed for the evaluation of elastic properties, where the minerals are studied individually (quartz and calcite) and as a composite (sandstone). The results of these tests are compared with reference values, showing difference percentages between 3 - 10% for the elastic modulus and between 0.7 - 2.1% for the Poisson's ratio.

PDF (Español (España))

Downloads

Download data is not yet available.

References

  1. C. S. Correa, “Metodología para estimar propiedades elásticas en muestras de rocas a través del uso de Física de Roca Digital (DRP). Caso de estudio: arenisca,” trabajo de fin de grado Universidad Industrial de Santander, 2019.
  2. Y. P. Goyes-Peñafiel, S. Khurama-Velasquez, O. Nikolaevich-Kovin, “Exploración de gilsonita usando tomografías de resistividad eléctrica con geometría tipo gradiente: Caso de estudio en Rionegro (Colombia),” Rev. UIS Ing., vol. 19, no. 2, pp. 77–84, 2020, doi: https://doi.org/10.18273/revuin.v19n2-2020008
  3. V. Cnudde and M. N. Boone, “High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications,” Earth-Science Rev., vol. 123, pp. 1–17, 2013, doi: https://doi.org/10.1016/j.earscirev.2013.04.003
  4. H. Andrä et al., “Digital rock physics benchmarks—Part I: Imaging and segmentation,” Comput. Geosci., vol. 50, pp. 25–32, 2013, doi: https://doi.org/10.1016/j.cageo.2012.09.005
  5. H. Andrä et al., “Digital rock physics benchmarks-Part II: Computing effective properties,” Comput. Geosci., vol. 50, pp. 33–43, Jan. 2013, doi: https://doi.org/10.1016/j.cageo.2012.09.008
  6. I. Varfolomeev, I. Yakimchuk, I. Safonov, “An application of deep neural networks for segmentation of microtomographic images of rock samples,” Computers, 2019, doi: https://doi.org/10.3390/computers8040072
  7. C. Madonna, B. S. G. Almqvist, E. H. Saenger, “Digital rock physics: Numerical prediction of pressure-dependent ultrasonic velocities using micro-CT imaging,” Geophys. J. Int., vol. 189, no. 3, pp. 1475–1482, 2012, doi: https://doi.org/10.1111/j.1365-246X.2012.05437.x
  8. D. Benavente, “Propiedades físicas y utilización de rocas ornamentales,” in Utilización de rocas y minerales industriales. Seminarios de la Sociedad Española de Mineralogía, 2, 2006.
  9. T. Farhana Faisal, A. Islam, M. S. Jouini, R. S. Devarapalli, M. Jouiad, M. Sassi, “Numerical prediction of carbonate elastic properties based on multi-scale imaging,” Geomech. Energy Environ., 2019, doi: https://doi.org/10.1016/j.gete.2019.100125
  10. N. Saxena et al., “Rock properties from micro-CT images: Digital rock transforms for resolution, pore volume, and field of view,” Adv. Water Resour., vol. 134, no. August, 2019, doi: https://doi.org/10.1016/j.advwatres.2019.103419
  11. Y. Wang, Q. Teng, X. He, J. Feng, T. Zhang, “CT-image of rock samples super resolution using 3D convolutional neural network,” Comput. Geosci., vol. 133, no. November 2018, p. 104314, 2019, doi: https://doi.org/10.1016/j.cageo.2019.104314
  12. S. Karimpouli, P. Tahmasebi, “Segmentation of digital rock images using deep convolutional autoencoder networks,” Comput. Geosci., vol. 126, no. October 2018, pp. 142–150, 2019, doi: https://doi.org/10.1016/j.cageo.2019.02.003
  13. S. A. Ardila Parra, H. G. Sánchez-Acevedo, O. A. González-Estrada, “Evaluation of damage to the lumbar spine vertebrae L5 by finite element analysis,” Respuestas, vol. 24, no. 1, pp. 50–55, 2019, doi: https://doi.org/10.22463/0122820X.1804
  14. B. Ferrandiz, M. Tur, E. Nadal, “Simulación estructural de espumas de aluminio a partir de imágenes 2D mediante la combinación de técnicas de homogeneización y machine learning,” Rev. UIS Ing., vol. 17, no. 2, pp. 223–240, 2017, doi: https://doi.org/10.18273/revuin.v17n2-2018020
  15. S. Chauhan et al., “Processing of rock core microtomography images: Using seven different machine learning algorithms,” Comput. Geosci., vol. 86, pp. 120–128, 2016, doi: https://doi.org/10.1016/j.cageo.2015.10.013
  16. X. Tang, Z. Jiang, S. Jiang, Z. Li, “Heterogeneous nanoporosity of the Silurian Longmaxi Formation shale gas reservoir in the Sichuan Basin using the QEMSCAN, FIB-SEM, and nano-CT methods,” Mar. Pet. Geol., vol. 78, pp. 99–109, 2016, doi: https://doi.org/10.1016/j.marpetgeo.2016.09.010
  17. C. Madonna et al., “Synchrotron-based X-ray tomographic microscopy for rock physics investigations,” 75th Eur. Assoc. Geosci. Eng. Conf. Exhib. 2013 Inc. SPE Eur. 2013 Chang. Front., vol. 78, no. 1, pp. 415–419, 2013, doi: https://doi.org/10.3997/2214-4609.20130344
  18. M. Andrew, “A quantified study of segmentation techniques on synthetic geological XRM and FIB-SEM images,” Comput. Geosci., vol. 22, no. 6, pp. 1503–1512, 2018, doi: https://doi.org/10.1007/s10596-018-9768-y
  19. P. Iassonov, T. Gebrenegus, M. Tuller, “Segmentation of X-ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures,” Water Resour. Res., vol. 45, no. 9, pp. 1–12, 2009, doi: https://doi.org/10.1029/2009WR008087
  20. N. Saxena, R. Hofmann, F. O. Alpak, J. Dietderich, S. Hunter, R. J. Day-Stirrat, “Effect of image segmentation & voxel size on micro-CT computed effective transport & elastic properties,” Mar. Pet. Geol., vol. 86, pp. 972–990, 2017, doi: https://doi.org/10.1016/j.marpetgeo.2017.07.004
  21. I. Gitman, H. Askes, L. Sluys, “Representative volume: Existence and size determination,” Eng. Fract. Mech., vol. 74, no. 16, pp. 2518–2534, 2007, doi: https://doi.org/10.1016/j.engfracmech.2006.12.021
  22. A. Kameda, J. Dvorkin, Y. Keehm, A. Nur, W. Bosl, “Permeability-porosity transforms from small sandstone fragments,” Geophysics, vol. 71, no. 1, pp. 11–19, 2006, doi: https://doi.org/10.1190/1.2159054
  23. N. Saxena et al., “Imaging and computational considerations for image computed permeability: Operating envelope of Digital Rock Physics,” Adv. Water Resour., vol. 116, no. March, pp. 127–144, 2018, doi: https://doi.org/10.1016/j.advwatres.2018.04.001
  24. N. Saxena, G. Mavko, R. Hofmann, N. Srisutthiyakorn, “Estimating permeability from thin sections without reconstruction: Digital rock study of 3D properties from 2D images,” Comput. Geosci., vol. 102, no. February, pp. 79–99, 2017, doi: https://doi.org/10.1016/j.cageo.2017.02.014
  25. N. Saxena et al., “References and benchmarks for pore-scale flow simulated using micro-CT images of porous media and digital rocks,” Adv. Water Resour., vol. 109, pp. 211–235, 2017, doi: https://doi.org/10.1016/j.advwatres.2017.09.007
  26. J. Dvorkin, N. Derzhi, E. Diaz, Q. Fang, “Relevance of computational rock physics,” Geophysics, vol. 76, no. 5, 2011, doi: https://doi.org/10.1190/geo2010-0352.1
  27. I. W. Farmer, Engineering properties of rocks. Dunfermline, Reino Unido: Better World Books Ltd, 1968.
  28. E. A. Winograd, S. Bosco, J. P. Álvarez, M. Álvarez Mendoza, D. Hryb, M. Sánchez, “Characterization of mechanical properties of rocks using numerical simulations and image analysis,” in 49th US Rock Mechanics / Geomechanics Symposium, 2015.
  29. R. D. Lama, V. S. Vutukuri, Handbook on mechanical properties of rocks. Clausthal, Germany: Trans Tech Publications, 1978.
  30. E. C. Pegg and H. S. Gill, “An open source software tool to assign the material properties of bone for ABAQUS finite element simulations,” J. Biomech., vol. 49, no. 13, pp. 3116–3121, 2016, doi: https://doi.org/10.1016/j.jbiomech.2016.07.037
  31. L. Miquel-González, G. Ortiz-Rabell, O. Castro-Castiñeira, “Tomography axial computerized technique application to improve Cuban oil fields seal and reservoir rocks characterization,” Boletín Ciencias la Tierra, no. 41, pp. 73–80, 2017, doi: https://doi.org/10.15446/rbct.n41.55046
  32. ANSYS Inc, ANSYS®Academic Research Mechanical, Release 19.2, Help System. Canonsburg: ANSYS, Inc, 2018.

Most read articles by the same author(s)

<< < 1 2