Tracer transport in a fractured chalk: X-ray CT characterization and digital-image-based (DIB) simulation

A digital-image-based simulation methodology is applied to evaluate the influence of heterogeneous porosity on the evolution of tracer concentrations in imaged tracer tests. Maps of computed tomography (CT)-number are calibrated relative to average porosity, and then thresholded to define porosity maps. These data are then used to automate the distribution of parameters within a finite element representation of the geometry. The technique is applied to characterize the variability of the porosity, the hydraulic conductivity, and the diffusivity for an artificially fractured chalk core (30 × 5 cm). X-ray CT was used both to characterize the initial condition of the core, and then to concurrently monitor the transport of an NaI tracer within the fracture and into the surrounding matrix. The X-ray CT imaging is used to characterize the heterogeneous rock porosity, based on which the hydraulic conductivity, and diffusivity of the chalk were defined and were directly imported into our newly developed three-dimensional FEMLAB-based multiple physics simulator. Numerical simulations have confirmed the observed tracer transport behaviors: (1) The different tracer-penetration distances imaged in the matrix above and below the horizontal fracture are indicative of a greater tracer mass penetrating into the lower matrix; and (2) Transport in the matrix below the fracture was enhanced. The computer simulated tracer concentration distributions compare favorably with those monitored by X-ray CT.

[1]  Jean-Louis Chermant,et al.  Why automatic image analysis? An introduction to this issue , 2001 .

[2]  E. Withjack,et al.  Characterization and saturation determination of reservoir metagraywacke from The Geysers corehole SB-15-D (USA), using Nuclear Magnetic Resonance Spectrometry and X-ray Computed Tomography , 2001 .

[3]  A. Kovscek,et al.  POROSITY AND PERMEABILITY EVOLUTION ACCOMPANYING HOT FLUID INJECTION INTO DIATOMITE , 2001 .

[4]  J. Schembre,et al.  A technique for measuring two-phase relative permeability in porous media via X-ray CT measurements , 2003 .

[5]  S. Nakashima.  DIFFUSIVITY OF IONS IN PORE WATER AS A QUANTITATIVE BASIS FOR ROCK DEFORMATION RATE ESTIMATES , 1995 .

[6]  C. Karacan,et al.  Behavior and effect of different coal microlithotypes during gas transport for carbon dioxide sequestration into coal seams , 2003 .

[7]  Manabu Takahashi,et al.  In situ visualization of fluid flow image within deformed rock by X-ray CT , 2003 .

[8]  Leslie George Tham,et al.  Numerical studies of the influence of microstructure on rock failure in uniaxial compression — Part I: effect of heterogeneity , 2000 .

[9]  J. Chermant,et al.  Some fields of applications of automatic image analysis in civil engineering , 2001 .

[10]  P. Zitha,et al.  Effect of Crossflow on Foam-Induced Diversion in Layered Formations , 2005 .

[11]  Paulo Cesar Philippi,et al.  Using three-dimensional reconstructed microstructures for predicting intrinsic permeability of reservoir rocks based on a Boolean lattice gas method , 2002 .

[12]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Leslie George Tham,et al.  A replica method for observing microcracks on rock surfaces , 2003 .

[14]  Leslie George Tham,et al.  Digital image-based numerical modeling method for prediction of inhomogeneous rock failure , 2004 .

[15]  Claudio Scavia,et al.  Determination of contact areas in rock joints by X-ray computer tomography , 1999 .

[16]  Xavier Maldague,et al.  An Evaluation of Image Analysis Algorithms for Constructing Discontinuity Trace Maps , 2003 .

[17]  P. Zitha,et al.  Effect of Capillary Cross-Flow on Foam-Induced Diversion in Layered Formations , 2003 .

[18]  Jun Zhao,et al.  A Study on the Quantitative Processing Methods of Imag Data , 2000 .

[19]  C. Tang,et al.  Numerical simulation of progressive rock failure and associated seismicity , 1997 .

[20]  S Finsterle,et al.  Migration of a water pulse through fractured porous media. , 2002, Journal of contaminant hydrology.

[21]  Vincent C. Tidwell,et al.  Effects of spatially heterogeneous porosity on matrix diffusion as investigated by X-ray absorption imaging , 1998 .

[22]  A. Becker,et al.  Designing a Monitoring Network for Contaminated Ground Water in Fractured Chalk , 1999 .

[23]  Chun’an Tang,et al.  Micromechanical Model for Simulating the Fracture Process of Rock , 2004 .

[24]  S. L. Wellington,et al.  Tomographic imaging of three‐phase flow experiments , 1987 .

[25]  Y. Nakashima.  The use of X-ray CT to measure diffusion coefficients of heavy ions in water-saturated porous media , 2000 .

[26]  S. C. Blair,et al.  Analysis of compressive fracture in rock using statistical techniques: Part I. A non-linear rule-based model , 1998 .

[27]  M. Zako,et al.  Microstructure-based stress analysis and evaluation for porous ceramics by homogenization method with digital image-based modeling , 2003 .

[28]  Rony Wallach,et al.  Tracer diffusion from a horizontal fracture into the surrounding matrix: measurement by computed tomography. , 2003, Journal of contaminant hydrology.

[29]  Mark L. Rivers,et al.  Using X-ray computed tomography in hydrology: systems, resolutions, and limitations , 2002 .

[30]  Carlo D. Montemagno,et al.  Fracture network versus single fractures: Measurement of fracture geometry with X-ray tomography , 1999 .

[31]  P. Philippi,et al.  3D reconstitution of porous media from image processing data using a multiscale percolation system , 2004 .

[32]  A. S. Murer,et al.  Permeability Damage in Diatomite Due to in-situ Silica Dissolution/Precipitation , 1996 .

[33]  P. Carman.  Fluid flow through granular beds , 1997 .

[34]  Leslie George Tham,et al.  Finite element modeling of geomaterials using digital image processing , 2003 .