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Volume 32 Issue 2
Apr 2021
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Weisheng Hou, Hengguang Liu, Tiancheng Zheng, Wenjie Shen, Fan Xiao. Hierarchical MPS-Based Three-Dimensional Geological Structure Reconstruction with Two-Dimensional Image(s). Journal of Earth Science, 2021, 32(2): 455-467. doi: 10.1007/s12583-021-1443-x
Citation: Weisheng Hou, Hengguang Liu, Tiancheng Zheng, Wenjie Shen, Fan Xiao. Hierarchical MPS-Based Three-Dimensional Geological Structure Reconstruction with Two-Dimensional Image(s). Journal of Earth Science, 2021, 32(2): 455-467. doi: 10.1007/s12583-021-1443-x

Hierarchical MPS-Based Three-Dimensional Geological Structure Reconstruction with Two-Dimensional Image(s)

doi: 10.1007/s12583-021-1443-x
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  • Corresponding author: Hou Weisheng, houwsh@mail.sysu.edu.cn
  • Received Date: 02 Dec 2020
  • Accepted Date: 15 Feb 2021
  • Publish Date: 01 Apr 2021
  • Multiple-point statistics (MPS) is a useful approach to reconstruct three-dimensional models in the macroscopic or microscopic field. Extracting spatial features for three-dimensional reconstruction from two-dimensional training images (TIs), and characterizing non-stationary features with directional ductility are two key issues in MPS simulation. This study presents a step-wise MPS-based three-dimensional structures reconstruction algorithm with the sequential process and hierarchical strategy based on two-dimensional images. An extension method is proposed to construct three-dimensional TIs. With a sequential simulation process, an initial guess at the coarsest scale is simulated, in which hierarchical strategy is used according to the characteristics of TIs. To obtain a more refined realization, an expectation-maximization like iterative process with global optimization is implemented. A concrete example of chondrite micro-structure simulation, in which one scanning electron microscopy (SEM) image of the Heyetang m eteorite is used as TI, shows that the presented algorithm can simulate complex non-stationary structures.

     

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  • Barnes, C., Shechtman, E., Finkelstein, A., et al., 2009. Patchmatch: A Randomized Correspondence Algorithm for Structural Image Editing. ACM Transactions on Graphics, 28: 1-11. https://doi.org/10.1145/1531326.1531330
    Chen, G. X., Zhao, F., Wang, J. G., et al., 2015. Regionalized Multiple-Point Stochastic Geological Modeling: A Case from Braided Delta Sedimentary Reservoirs in Qaidam Basin, NW China. Petroleum Exploration and Development, 42(5): 697-704. https://doi.org/10.1016/S1876-3804(15)30065-3
    Chen, Q. Y., Mariethoz, G., Liu, G., et al., 2018. Locality-Based 3-D Multiple-Point Statistics Reconstruction Using 2-D Geological Cross Sections. Hydrology and Earth System Sciences, 22(12): 6547-6566. https://doi.org/10.5194/hess-22-6547-2018
    Comunian, A., Giudici, M., Landoni, L., et al., 2018. Improving Bowen-Ratio Estimates of Evaporation Using a Rejection Criterion and Multiple-Point Statistics. Journal of Hydrology, 563: 43-50. https://doi.org/10.1016/j.jhydrol.2018.05.050
    Comunian, A., Renard, P., Straubhaar, J., 2012. 3D Multiple-Point Statistics Simulation Using 2D Training Images. Computers & Geosciences, 40: 49-65. https://doi.org/10.1016/j.cageo.2011.07.009
    Dimitrakopoulos, R., Mustapha, H., Gloaguen, E., 2009. High-Order Statistics of Spatial Random Fields: Exploring Spatial Cumulants for Modeling Complex Non-Gaussian and Non-Linear Phenomena. Mathematical Geosciences, 42(1): 65-99. https://doi.org/10.1007/s11004-009-9258-9
    Feng, W. J., Wu, S. H., Yin, Y. S., et al., 2017. A Training Image Evaluation and Selection Method Based on Minimum Data Event Distance for Multiple-Point Geostatistics. Computers & Geosciences, 104: 35-53. https://doi.org/10.1016/j.cageo.2017.04.004
    Gueting, N., Caers, J., Comunian, A., et al., 2017. Reconstruction of Three-Dimensional Aquifer Heterogeneity from Two-Dimensional Geophysical Data. Mathematical Geosciences, 50(1): 53-75. https://doi.org/10.1007/s11004-017-9694-x
    Houlding, S. W., 1994. 3D Geoscience Modeling Computer Techniques for Geological Characterization. Springer-Verlag, Berlin Heidelberg
    Hu, L. Y., Liu, Y., Scheepens, C., et al., 2013. Multiple-Point Simulation with an Existing Reservoir Model as Training Image. Mathematical Geosciences, 46(2): 227-240. https://doi.org/10.1007/s11004-013-9488-8
    Ji, L. L., Lin, M., Jiang, W. B., et al., 2017. An Improved Method for Reconstructing the Digital Core Model of Heterogeneous Porous Media. Transport in Porous Media, 121(2): 389-406. https://doi.org/10.1007/s11242-017-0970-5
    Kaufmann, O., Martin, T., 2008. 3D Geological Modelling from Boreholes, Cross-Sections and Geological Maps, Application over Former Natural Gas Storages in Coal Mines. Computers & Geosciences, 34(3): 278-290. https://doi.org/10.1016/j.cageo.2007.09.005
    Lessenger, M., Gladczenko, T., Hardt, J., et al., 2019. Facies-Calibrated Petrophysical and Geocellular Property Modeling Using Data Analytics and Multi-Point Statistics in the Delaware Basin. SPE/AAPG/SEG Unconventional Resources Technology Conference, July 2019, Denver. https://doi.org/10.15530/urtec-2019-421
    Li, J. Q., Zhang, P. F., Lu, S. F., et al., 2019. Scale-Dependent Nature of Porosity and Pore Size Distribution in Lacustrine Shales: An Investigation by BIB-SEM and X-Ray CT Methods. Journal of Earth Science, 30(4): 823-833. https://doi.org/10.1007/s12583-018-0835-z
    Li, Y., Teng, Q. Z., He, X. H., et al., 2019. Super-Dimension-Based Three-Dimensional Nonstationary Porous Medium Reconstruction from Single Two-Dimensional Image. Journal of Petroleum Science and Engineering, 174: 968-983. https://doi.org/10.1016/j.petrol.2018.12.004
    Ma, B. J., Wu, S. G., Mi, L. J., et al., 2018. Mixed Carbonate-Siliciclastic Deposits in a Channel Complex in the Northern South China Sea. Journal of Earth Science, 29(3): 707-720. https://doi.org/10.1007/s12583-018-0830-4
    Mariethoz, G., Caers, J., 2014. Multiple-Point Geostatistics. John Wiley & Sons, Chichester. https://doi.org/10.1002/9781118662953
    Mariethoz, G., Renard, P., Straubhaar, J., 2010. The Direct Sampling Method to Perform Multiple-Point Geostatistical Simulations. Water Resources Research, 46(11): W11536. https://doi.org/10.1029/2008wr007621
    Mohammadmoradi, P., Institute of Petroleum Engineering University of Tehran Iran, Rasaeii, M., et al., 2014. Reconstruction of Non-Stationary Complex Spatial Structures by a Novel Filter-Based Multi Scale Mps Algorithm. Open Transactions on Geosciences, 2014(2): 60-71. https://doi.org/10.15764/geos.2014.02007
    Pyrcz, M. J., Deutsch, C. V., 2014. Geostatistical Reservoir Modeling. Oxford University Press, Oxford
    Shen, W., Hu, S., Lin, Y., et al., 2013. Chemical and Petrologic Study of the Heyetang Meteorite. Chinese Journal of Polar Research, 25(4): 386-393. https://doi.org/10.3724/sp.J.1084.2013.00386 (in Chinese wtih English Abstract) doi: 10.3724/sp.J.1084.2013.00386(inChinesewtihEnglishAbstract)
    Song, W. H., Yao, J., Ma, J. S., et al., 2018. Pore-Scale Numerical Investigation into the Impacts of the Spatial and Pore-Size Distributions of Organic Matter on Shale Gas Flow and Their Implications on Multiscale Characterisation. Fuel, 216:707-721. https://doi.org/10.1016/j.fuel.2017.11.114
    Strebelle, S., 2002. Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics. Mathematical Geology, 34(1): 1-21. https://doi.org/10.1023/a:1014009426274
    Tahmasebi, P., Hezarkhani, A., Sahimi, M., 2012. Multiple-Point Geostatistical Modeling Based on the Cross-Correlation Functions. Computational Geosciences, 16(3): 779-797. https://doi.org/10.1007/s10596-012-9287-1
    Tahmasebi, P., Sahimi, M., 2012. Reconstruction of Three-Dimensional Porous Media Using a Single Thin Section. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 85(6): 066709. https://doi.org/10.1103/physreve.85.066709
    Tahmasebi, P., Sahimi, M., 2015. Reconstruction of Nonstationary Disordered Materials and Media: Watershed Transform and Cross-Correlation Function. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 91(3): 032401. https://doi.org/10.1103/physreve.91.032401
    Tang, Y. W., Atkinson, P. M., Zhang, J. X., 2015. Downscaling Remotely Sensed Imagery Using Area-to-Point Cokriging and Multiple-Point Geostatistical Simulation. ISPRS Journal of Photogrammetry and Remote Sensing, 101:174-185. https://doi.org/10.1016/j.isprsjprs.2014.12.016
    Western, A. W., Blöschl, G., Grayson, R. B., 2001. Toward Capturing Hydrologically Significant Connectivity in Spatial Patterns. Water Resources Research, 37(1): 83-97. https://doi.org/10.1029/2000wr900241
    Yang, L., Hou, W. S., Cui, C. J., et al., 2016. GOSIM: a Multi-Scale Iterative Multiple-Point Statistics Algorithm with Global Optimization. Computers & Geosciences, 89:57-70. https://doi.org/10.1016/j.cageo.2015.12.020
    Zhang, T. F., Gelman, A., Laronga, R., 2017. Structure-and Texture-Based Fullbore Image Reconstruction. Mathematical Geosciences, 49(2): 195-215. https://doi.org/10.1007/s11004-016-9649-7
    Zhang, W. B., Duan, T. Z., Liu, Z. Q., et al., 2016. Application of Multi-Point Geostatistics in Deep-Water Turbidity Channel Simulation: a Case Study of Plutonio Oilfield in Angola. Petroleum Exploration and Development, 43(3): 443-450. https://doi.org/10.1016/S1876-3804(16)30051-9
    Zhou, Z. C., Mei, L. F., Shi, H. S., et al., 2019. Evolution of Low-Angle Normal Faults in the Enping Sag, the Northern South China Sea: Lateral Growth and Vertical Rotation. Journal of Earth Science, 30(6): 1326-1340. https://doi.org/10.1007/s12583-019-0899-4
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