Quantifying the heterogeneity of shale through statistical combination of imaging across scales

Shale is a highly heterogeneous material across multiple scales. A typical shale consists of nanometer-scale pores and minerals mixed with macroscale fractures and particles of varying size. High-resolution imaging is crucial for characterizing the composition and microstructure of this rock. However, it is generally not feasible to image a large sample of shale at a high resolution over a large field of view (FOV), thus limiting a full characterization of the microstructure of this material. We present a stochastic framework based on multiple-point statistics that uses high-resolution training images to enhance low-resolution images obtained over a large FOV. We demonstrate the approach using X-ray micro-tomography images of organic-rich Woodford shale obtained at two different resolutions and FOV. Results show that the proposed technique can generate realistic high-resolution images of the microstructure of shale over a large FOV.

[1]  COMPUTATIONS OF POROSITY AND PERMEABILITY OF SPARIC CARBONATE USING MULTI-SCALE CT IMAGES , 2009 .

[2]  Yijin Liu,et al.  Three-dimensional imaging of chemical phase transformations at the nanoscale with full-field transmission X-ray microscopy. , 2011, Journal of synchrotron radiation.

[3]  Ronaldo I. Borja,et al.  Shear band in sand with spatially varying density , 2013 .

[4]  Dorthe Wildenschild,et al.  Image processing of multiphase images obtained via X‐ray microtomography: A review , 2014 .

[5]  H. Lemmens,et al.  4 Multiscale Imaging of Shale Samples in the Scanning Electron Microscope , 2013 .

[6]  Jianfeng Wang,et al.  Generation of a realistic 3D sand assembly using X‐ray micro‐computed tomography and spherical harmonic‐based principal component analysis , 2017 .

[7]  Ronaldo I. Borja,et al.  Mathematical framework for unsaturated flow in the finite deformation range , 2014 .

[8]  Tariq M. AlGhamdi,et al.  Shale Gas Characterization and Property Determination by Digital Rock Physics , 2014 .

[9]  Philippe Renard,et al.  A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm , 2013, Comput. Geosci..

[10]  Ting Zhang,et al.  Porous Media Reconstruction Using a Cross-Section Image and Multiple-Point Geostatistics , 2009, 2009 International Conference on Advanced Computer Control.

[11]  Dirk Mallants,et al.  Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock , 2015, Scientific Reports.

[12]  Roussos G. Dimitrakopoulos,et al.  A new approach for geological pattern recognition using high-order spatial cumulants , 2010, Comput. Geosci..

[13]  K. Alshibli,et al.  3D finite element modeling of sand particle fracture based on in situ X‐Ray synchrotron imaging , 2016 .

[14]  Yijin Liu,et al.  Nanoscale Visualization of Gas Shale Pore and Textural Features , 2013 .

[15]  Luke Hunter,et al.  A Path Toward Non-Destructive 3D Metrology for Through-Silicon Vias , 2011 .

[16]  Gaël Combe,et al.  FEM × DEM modelling of cohesive granular materials: Numerical homogenisation and multi-scale simulations , 2014, Acta Geophysica.

[17]  János Urai,et al.  Multi-scale characterization of porosity in Boom Clay (HADES-level, Mol, Belgium) using a combination of X-ray μ-CT, 2D BIB-SEM and FIB-SEM tomography , 2015 .

[18]  Giovanni Grasselli,et al.  Characterization of the effect of normal load on the discontinuity morphology in direct shear specimens using X-ray micro-CT , 2015 .

[19]  D. Wildenschild,et al.  X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems , 2013 .

[20]  Alexei A. Efros,et al.  Image quilting for texture synthesis and transfer , 2001, SIGGRAPH.

[21]  Karen O. Egiazarian,et al.  Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction , 2013, IEEE Transactions on Image Processing.

[22]  Gioacchino Viggiani,et al.  Multiscale modeling and characterization of granular matter: From grain kinematics to continuum mechanics , 2011 .

[23]  M. Blunt,et al.  Pore space reconstruction using multiple-point statistics , 2005 .

[24]  Ning Guo,et al.  Multiscale insights into classical geomechanics problems , 2016 .

[25]  R. Borja,et al.  Discrete micromechanics of elastoplastic crystals in the finite deformation range , 2014 .

[26]  S. Litster,et al.  Resolving the Three‐Dimensional Microstructure of Polymer Electrolyte Fuel Cell Electrodes using Nanometer‐Scale X‐ray Computed Tomography , 2012 .

[27]  W. V. Pinczewski,et al.  Permeability Upscaling for Carbonates from the Pore-Scale Using Multi-Scale Xray-CT Images , 2012 .

[28]  Yi Du,et al.  Reconstruction of porous media using multiple-point statistics with data conditioning , 2015, Stochastic Environmental Research and Risk Assessment.

[29]  Ronaldo I. Borja,et al.  Cam-Clay plasticity, Part VIII: A constitutive framework for porous materials with evolving internal structure , 2016 .

[30]  Kenneth Stuart Sorbie,et al.  3D Stochastic Modelling of Heterogeneous Porous Media – Applications to Reservoir Rocks , 2006 .

[31]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[32]  Joshua A. White,et al.  Thermoplasticity and strain localization in transversely isotropic materials based on anisotropic critical state plasticity , 2016 .

[33]  A. Soares.  Direct Sequential Simulation and Cosimulation , 2001 .

[34]  Gregoire Mariethoz,et al.  The Direct Sampling method to perform multiple‐point geostatistical simulations , 2010 .

[35]  F. Ulm,et al.  Nanomechanics of organic-rich shales: the role of thermal maturity and organic matter content on texture , 2016 .

[36]  Y. Han,et al.  The granular and polymer composite nature of kerogen-rich shale , 2016 .

[37]  Mark A. Knackstedt,et al.  Pore scale characterization of carbonates at multiple scales: integration of micro-CT, BSEM and FIBSEM , 2010 .

[38]  Gregoire Mariethoz,et al.  Integrating multiple scales of hydraulic conductivity measurements in training image‐based stochastic models , 2013 .

[39]  Joel Walls,et al.  Shale Reservoir Properties from Digital Rock Physics , 2012 .

[40]  Sebastien Strebelle,et al.  Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics , 2002 .

[42]  Christoph H. Arns,et al.  Pore Scale Characterization of Carbonates Using X-Ray Microtomography , 2005 .

[43]  M. Curtis,et al.  Microstructural investigation of gas shales in two and three dimensions using nanometer-scale resolution imaging , 2012 .

[44]  L. Y. Hu,et al.  Multiple‐point geostatistics for modeling subsurface heterogeneity: A comprehensive review , 2008 .

[45]  F. Ulm,et al.  Geomechanics Field and Laboratory Characterization of the Woodford Shale: The Next Gas Play , 2007 .

[46]  W. Nix,et al.  Instrumented nanoindentation and 3D mechanistic modeling of a shale at multiple scales , 2015 .

[47]  Pierre M. Adler,et al.  Flow in simulated porous media , 1990 .

[48]  Andre G. Journel,et al.  Spatial Connectivity: From Variograms to Multiple-Point Measures , 2003 .

[49]  José E. Andrade,et al.  Critical state plasticity. Part VI: Meso-scale finite element simulation of strain localization in discrete granular materials , 2006 .

[50]  R. Borja,et al.  Finite Deformation and Fluid Flow in Unsaturated Soils with Random Heterogeneity , 2014 .

[51]  D. Dewhurst,et al.  Laboratory characterisation of shale properties , 2012 .

[52]  A. Ghassemi,et al.  Large‐scale poroelastic fractured reservoirs modeling using the fast multipole displacement discontinuity method , 2016 .

[53]  G. Mariéthoz,et al.  Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations , 2013 .

[54]  Gioacchino Viggiani,et al.  Volumetric Digital Image Correlation Applied to X‐ray Microtomography Images from Triaxial Compression Tests on Argillaceous Rock , 2007 .

[55]  B. Schröppel.  Multi-scale analysis of porosity in diagenetically altered reservoir sandstone from the Permian Rotliegend (Germany) , 2016 .

[56]  P. Gasser,et al.  Characterization of multi-scale microstructural features in Opalinus Clay , 2013 .

[57]  R. Borja,et al.  On the pore‐scale mechanisms leading to brittle and ductile deformation behavior of crystalline rocks , 2015 .

[58]  R. Borja,et al.  Pore‐scale modeling of deformation and shear band bifurcation in porous crystalline rocks , 2016 .

[59]  V. Hejtmánek,et al.  Stochastic Reconstruction of Particulate Media Using Simulated Annealing: Improving Pore Connectivity , 2009 .

[60]  Hilfer,et al.  Stochastic reconstruction of sandstones , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[61]  Younane N. Abousleiman,et al.  Gassmann equations and the constitutive relations for multiple‐porosity and multiple‐permeability poroelasticity with applications to oil and gas shale , 2015 .

[62]  G. Viggiani,et al.  Strain localisation and grain breakage in sand under shearing at high mean stress: insights from in situ X-ray tomography , 2015 .

[63]  Fred P. Wang,et al.  Pore Networks and Fluid Flow in Gas Shales , 2009 .

[64]  R. Borja,et al.  Computational Aspects of Elasto-Plastic Deformation in Polycrystalline Solids , 2012 .

[65]  Franz-Josef Ulm,et al.  Nanochemo-mechanical signature of organic-rich shales: a coupled indentation–EDX analysis , 2016 .

[66]  Veerle Cnudde,et al.  High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications , 2013 .

[67]  L. Feyen,et al.  Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations , 2006 .