Multi-Scale Image Fusion of X-Ray Microtomography and SEM Data to Model Flow and Transport Properties for Complex Rocks on Pore-Level
- Kirill M. Gerke (Institute of Geospheres Dynamics of Russian Academy of Sciences, Schmidt's Institute of Physics of the Earth of Russian Academy of Sciences, Dokuchaev Soil Science Institute of Russian Academy of Sciences) | Marina V. Karsanina (Institute of Geospheres Dynamics of Russian Academy of Sciences, Schmidt's Institute of Physics of the Earth of Russian Academy of Sciences, Dokuchaev Soil Science Institute of Russian Academy of Sciences) | Timofey O. Sizonenko (Institute of Geospheres Dynamics of Russian Academy of Sciences, Schmidt's Institute of Physics of the Earth of Russian Academy of Sciences, Dokuchaev Soil Science Institute of Russian Academy of Sciences) | Xiuxiu Miao (Key Laboratory of High-efficient Mining and Safety of Metal Mines Ministry of Education, University of Science and Technology Beijing) | Dina R. Gafurova (Lomonosov Moscow State University) | Dmitry V. Korost (Lomonosov Moscow State University)
- Document ID
- Society of Petroleum Engineers
- SPE Russian Petroleum Technology Conference, 16-18 October, Moscow, Russia
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 5 Reservoir Desciption & Dynamics, 1.2.3 Rock properties, 5.3.2 Multiphase Flow, 5.5.3 Scaling Methods, 5.3 Reservoir Fluid Dynamics, 5.1 Reservoir Characterisation, 5.5 Reservoir Simulation, 1.6.9 Coring, Fishing, 1.6 Drilling Operations
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Pore-level imaging and modelling were shown to be robust and useful techniques, at least if applied to conventional rocks such as sandstones. This type of modelling directly within the pore space of the imaged samples provides valuable insights into rock heterogeneity, estimates local single and multi-phase flow properties, and serves as a key tool for upscaling and parameterizing Darcian models. Yet, numerous problems are still to be solved related to rocks with complex and hierarchical structure, such as carbonates, shales and coals. These rocks possess pore sizes in a wide range of values which has to be imaged with different resolutions in order to capture all relevant pore scales. This is due to so-called sample size/imaging resolution trade-off. To develop a detailed 3D structure model, such rocks are imaged using different resolutions and even using different imaging techniques. The problem lies with combining all these multiscale images into a single 3D digital structure model. In this work the recently developed multiscale image fusion technique was tested on complex carbonate samples with hierarchical structure. For two samples we performed a detailed structural study on two different scales: 3D XCT scanning (2.7 µm resolution) and 2D SEM imaging (0.9 µm pixel size). These two scales were fused to represent carbonate rocks structure with the predefined resolution of 0.9 µm and volume of 15003 voxels combining structural features discernible on both XCT and SEM images. Fused 3D images were used as input data to a hybrid median axis/maximum inscribed ball pore-network technique with subsequent modelling of permeability. Resulting simulated values were compared with laboratory measurement on the cores with dimeter of 5 cm. For the Sample 1 micropores visible on XCT scan were not connected, thus, preventing any flow simulations. After fusion with SEM image simulated permeability agreed favourably with the measurements. For the Sample 2 micropore network was percolating, but simulated permeability was lower than the experimental one. Incorporating sub-resolution porosity in this sample by adding SEM finer porosity structure resulted in higher permeability value very close to the laboratory measurement. In this contribution we also discuss why simulated and measured permeability values do not agree perfectly, which is most likely due to the scale difference between the volumes of simulated and measurement domains. We also covered all major drawbacks of the multiscale image fusion techniques and discussed possible solutions. Current study clearly showed the potential of this novel approach to facilitate pore-level modelling of flow and transport in rocks with complex and hierarchical structure such as carbonates, shales and coals. We believe that after some improvements and rigorous testing multiscale fusion technique may become a core tool in imaging and pore-level modelling of flow properties for complex rocks with hierarchical structure.
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Blunt, M.J., Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S., Mostaghimi, P., Paluszny, A. and Pentland, C. (2013) Pore-scale imaging and modelling. Advances in Water Resources 51, 197-216. DOI: 10.1016/j.advwatres.2012.03.003.
Bultreys, T., Van Hoorebeke, L., & Cnudde, V. (2015). Multi-scale, micro-computed tomography-based pore network models to simulate drainage in heterogeneous rocks. Advances in Water Resources, 78, 36-49. DOI: 10.1016/j.advwatres.2015.02.003
Cnudde, V. and Boone, M.N. (2013) High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Reviews 123, 1-17. DOI: 10.1016/j.earscirev.2013.04.003
Dong, H. and Blunt, M.J. (2009) Pore-network extraction from micro-computerized-tomography images. Physical Review E 80(3), 036307. DOI: 10.1103/PhysRevE.80.036307.
Gerke, K.M., Karsanina, M. V, Mallants, D., 2015a. Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock. Sci. Rep. 5, 15880. doi: 10.1038/srep15880
Gerke, K.M., Karsanina, M. V., Vasilyev, R. V., Mallants, D., 2014. Improving pattern reconstruction using directional correlation functions. EPL (Europhysics Lett. 106, 66002. doi: 10.1209/0295-5075/106/66002
Gerke, K.M., Karsanina, M. V., 2015. Improving stochastic reconstructions by weighting correlation functions in an objective function. EPL (Europhysics Lett. 111, 56002. doi: 10.1209/0295-5075/111/56002
Gerke KM, Vasilyev RV, Korost DV, Karsanina MV, Balushkina NS, Khamidullin R, . Determining physical properties of unconventional reservoir rocks: from laboratory methods to pore-scale modeling. 2013; DOI: 10.2118/167058-MS.
Gerke, K. M., Karsanina, M. V., Skvortsova, E. B., 2012b. Description and reconstruction of the soil pore space using correlation functions. Eurasian Soil Sci. 45(9), 861-872. doi: 10.1134/S1064229312090049
Gerke, K.M., Skvortsova, E.B., Korost, D. V., 2012a. Tomographic method of studying soil pore space: Current perspectives and results for some Russian soils. Eurasian Soil Sci. 45, 700–709. doi:10.1134/S1064229312070034
Gerke K.M., Vasilyev R.V., Khirevich S., Karsanina M.V., Collins D., Sizonenko T., Korost D.V., Lamontagne S., Mallants D. (2017) Finite-difference method Stokes solver (FDMSS) for permeability and flow velocity fields in 3D pore geometries: software development, validation and case studies (submitted to Computers & Geosciences).
Godinho, J.R.A., Gerke, K.M., Stack, A.G., Lee, P.D., 2016. The dynamic nature of crystal growth in pores. Sci. Rep. 6, 33086. doi: 10.1038/srep33086
Hashemi, M. A., Khaddour, G., François, B., Massart, T. J., & Salager, S. (2014). A tomographic imagery segmentation methodology for three-phase geomaterials based on simultaneous region growing. Acta Geotechnica, 9(5), 831-846. DOI: 10.1007/s11440-013-0289-5
Havelka, J., Kucerová, A., Sýkora, J., 2016. Compression and reconstruction of random microstructures using accelerated lineal path function. Comput. Mater. Sci. 122, 102–117. doi:10.1016/j.commatsci.2016.04.044
Holmes, D.W., Williams, J.R., Tilke, P. and Leonardi, C.R. (2016) Characterizing flow in oil reservoir rock using SPH: Absolute permeability. Computational Particle Mechanics 3(2), 141-154. DOI: 10.1007/s40571-015-0038-7.
Jiao, Y., Chawla, N., 2014. Modeling and characterizing anisotropic inclusion orientation in heterogeneous material via directional cluster functions and stochastic microstructure reconstruction. J. Appl. Phys. 115, 093511. doi: 10.1063/1.4867611
Jiao, Y., Stillinger, F.H. and Torquato, S. (2009) A superior descriptor of random textures and its predictive capacity. Proceedings of the National Academy of Sciences of the United States of America 106(42), 17634-17639. DOI: 10.1073/pnas.0905919106
Ju, Y., Huang, Y., Zheng, J., Qian, X., Xie, H., & Zhao, X. (2017). Multi-thread parallel algorithm for reconstructing 3D large-scale porous structures. Computers & Geosciences, 101, 10-20. DOI: 10.1016/j.cageo.2017.01.003
Karsanina, M. V., Gerke, K.M., Skvortsova, E.B., Mallants, D., 2015. Universal spatial correlation functions for describing and reconstructing soil microstructure. PLoS One 10, e0126515. doi: 10.1371/journal.pone.0126515
Khirevich, S., Ginzburg, I. and Tallarek, U. (2015) Coarse- and fine-grid numerical behavior of MRT/TRT lattice-Boltzmann schemes in regular and random sphere packings. Journal of Computational Physics 281, 708-742. DOI: 10.1016/j.jcp.2014.10.038
Kim, J.W., Kim, D. and Lindquist, W. (2013) A re-examination of throats. Water Resources Research 49(11), 7615-7626. DOI: 10.1002/2013WR014254.
Korost D.V., Gerke K.M. (2012) Computation of reservoir properties based on 3D-structure of porous media. SPE 162023 Technical paper, presented at SPE Russian Oil and Gas Exploration and Production Technical Conference and Exhibition, 16-18 October, Moscow, Russia. DOI: 10.2118/162023-MS.
Kulkarni, R., Tuller, M., Fink, W., & Wildenschild, D. (2012). Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework. Vadose Zone Journal, 11. DOI: 10.2136/vzj2011.0082
Lindquist, W.B., Lee, S.M., Coker, D.A., Jones, K.W. and Spanne, P. (1996) Medial axis analysis of void structure in three-dimensional tomographic images of porous media. Journal of Geophysical Research: Solid Earth 101(B4), 8297-8310. DOI: 10.1029/95JB03039.
Mariethoz, G., Renard, P. and Straubhaar, J. (2011) Extrapolating the fractal characteristics of an image using scale-invariant multiple-point statistics. Mathematical Geosciences 43(7), 783-797. DOI: 10.1007/s11004-011-9362-5.
Miao X., Gerke K.M., Sizonenko T.O., 2017. A new way to parameterize hydraulic conductances of pore elements: A step forward to create pore-networks without pore shape simplifications. Adv. Water Resour. 105, 162–172. doi:10.1016/j.advwatres.2017.04.021
Mostaghimi, P., Blunt, M.J., Bijeljic, B. (2013) Computations of Absolute Permeability on Micro-CT Images. Math. Geosci. 45, 103–125. DOI: 10.1007/s11004-012-9431-4.
Mostaghimi P., Armstrong R.T., Gerami A., Hu J., Jing Y, Kamali F., Liu M., Liu Z., Lu X., Ramandi H.L., Zamani A., Zhang Y., 2017. Cleat-scale characterisation of coal: An overview. Journal of Natural Gas Science and Engineering, 39: 143-160. DOI: 10.1016/j.jngse.2017.01.025.
Oh, W., Lindquist, B., W., 1999. Image thresholding by indicator kriging. IEEE Trans. Pattern Anal. Mach. Intell. 21, 590–602. doi:10.1109/34.777370
Oren, P.-E., Bakke, S. and Arntzen, O.J. (1998) Extending predictive capabilities to network models. SPE Journal 3(04), 324-336. DOI: 10.2118/52052-PA.
Patzek, T.W. (2000) Verification of a complete pore network simulator of drainage and imbibition. SPE Journal 6(2), 144-156. DOI: 10.2118/71310-PA.
Ryazanov, A., van Dijke, M.I.J. and Sorbie, K.S. (2009) Two-phase pore-network modelling: Existence of oil layers during water invasion. Transport in Porous Media 80(1), 79-99. DOI: 10.1007/s11242-009-9345-x.
Raeini, A.Q., Blunt, M.J. and Bijeljic, B. (2012) Modelling two-phase flow in porous media at the pore scale using the volume-of-fluid method. Journal of Computational Physics 231(17), 5653-5668. DOI: 10.1016/j.jcp.2012.04.011.
Shah, S. M., Gray, F., Crawshaw, J. P., & Boek, E. S. (2016). Micro-computed tomography pore-scale study of flow in porous media: Effect of voxel resolution. Advances in Water Resources, 95, 276-287. DOI: 10.1016/j.advwatres.2015.07.012
Sedaghat, M. H., Gerke, K., Azizmohammadi, S., & Matthai, S. K. (2016). Simulation-based Determination of Relative Permeability in Laminated Rocks. Energy Procedia, 97, 433-439. DOI: 10.1016/j.egypro.2016.10.041
Sheppard, A.P., Sok, R.M. and Averdunk, H. (2004) Techniques for image enhancement and segmentation of tomographic images of porous materials. Physica A: Statistical mechanics and its applications 339(1), 145-151. DOI: 10.1016/j.physa.2004.03.057.
Silin, D. and Patzek, T. (2006) Pore space morphology analysis using maximal inscribed spheres. Physica A: Statistical mechanics and its applications 371(2), 336-360. DOI: 10.1016/j.physa.2006.04.048.
Tahmasebi, P. and Sahimi, M. (2013) Cross-correlation function for accurate reconstruction of heterogeneous media. Physical Review Letters 110(7). DOI: 10.1103/PhysRevLett.110.078002
Valvatne, P.H. and Blunt, M.J. (2004) Predictive pore-scale modeling of two-phase flow in mixed wet media. Water Resources Research 40(7), W07406. DOI: 10.1029/2003wr002627.
Vesely M., Bultreys T., Peksa M., Lang J., Cnudde V., van Hoorebeke L., Kocirik M., Hejtmanek V., Solcova O., Soukup K., Gerke K.M., Stallmach F., Capek P. Prediction and evaluation of time-dependent effective self-diffusivity of water and other effective transport properties associated with reconstructed solids. Transport in Porous Media, 2015, 110(1): 81-111, DOI: 10.1007/s11242-015-0557-y.
Wildenschild, D. and Sheppard, A.P. (2013) X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems. Advances in Water Resources 51, 217-246. DOI: 10.1016/j.advwatres.2012.07.018.
Xiong, Q., Joseph, C., Schmeide, K., Jivkov, A.P. (2015) Measurement and modelling of reactive transport in geological barriers for nuclear waste containment. Physical Chemistry Chemical Physics 17(45) 30577-30589. DOI: 10.1039/C5CP05243B.
Yeong, C., Torquato, S., 1998. Reconstructing random media. II. Three-dimensional media from two-dimensional cuts. Phys. Rev. E 58, 224–233. doi:10.1103/PhysRevE.58.224
Zaretskiy, Y., Geiger, S., Sorbie, K. and Forster, M. (2010) Efficient flow and transport simulations in reconstructed 3D pore geometries. Advances in Water Resources 33(12), 1508-1516. DOI: 10.1016/j.advwatres.2010.08.008.