Characterization of elastic properties in a sandstone rock sample using digital rock physics
Published 2022-02-11
Keywords
- finite element method,
- digital rock physics,
- elastic properties,
- sandstone
How to Cite
Copyright (c) 2022 Revista UIS Ingenierías
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.
Downloads
References
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- I. W. Farmer, Engineering properties of rocks. Dunfermline, Reino Unido: Better World Books Ltd, 1968.
- 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.
- R. D. Lama, V. S. Vutukuri, Handbook on mechanical properties of rocks. Clausthal, Germany: Trans Tech Publications, 1978.
- 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
- 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
- ANSYS Inc, ANSYS®Academic Research Mechanical, Release 19.2, Help System. Canonsburg: ANSYS, Inc, 2018.