Interpretation of out-diffusion experiments on crystalline rocks using random walk modeling

Sardini, Paul; Delay, Frédérick; Hellmuth, Karl-Heinz; Porel, Gilles; Oila, Esa

doi:10.1016/s0169-7722(02)00124-9

Cited by 34 publications

(17 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This method is briefly presented here, and the reader is invited to consult Delay et al (2002) and Sardini et al (2003Sardini et al ( , 2007 for a more detailed description. Note here that some other numerical techniques resolve the unsteady diffusion equations (Borisova and Adler 2006;Ackerer and Mosé 2000;among others).…”

Section: Numerical Modeling Of Residence Time Distribution Using the mentioning

confidence: 99%

“…Fractures represent fast pathways for solute migration, whereas matrix diffusion controls the retardation of solutes (Neretnieks 1980). Many diffusion experiments carried out in laboratory on rock samples of centimetric size have proved that diffusion properties depend on the spatial distribution of porosity and diffusion coefficients (Bradbury and Green 1985;Moreno et al 1985;Siitari-Kauppi et al 1994;Sardini et al 2003;Tidwell et al 2000). In order to integrate the matrix diffusion term into a transport model, Carrera et al (1998) and Haggerty et al (2000) suggest using the Residence Time Distribution (RTD) of a solute in the matrix in the source-sink term of the advectiondispersion equation.…”

Section: Introductionmentioning

confidence: 99%

“…First, a particle tracking method was developed and validated in order to compute RTDs in heterogeneous matrices using two-dimensional porosity and diffusion coefficient maps. The Time Domain Diffusion method (TDD) was employed, in order to simulate the diffusion process over large pixel grids (Delay et al 2002;Sardini et al 2003). Second, RTDs were calculated for virtual porous networks that are equivalent to commonly observed structures: fracture coatings and porosity gradients.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Effect of Rock Matrix Heterogeneities Near Fracture Walls on the Residence Time Distribution (RTD) of Solutes

et al. 2007

Self Cite

View full text Add to dashboard Cite

For interpreting solute transport experiments within rocks, the Residence Time Distributions (RTDs) of solutes in the rock matrix were either derived from the assumption of a homogeneous matrix, or were considered through analytical distributions of diffusion coefficients. A numerical approach based on a Lagrangian framework was developed in order to investigate the effect of spatial heterogeneities on RTDs. The matrix diffusion was simulated over two-dimensional computation grids, representing virtual or real digitized porosity maps. First, virtual porosity maps were used to mimic porous features linked to conductive fractures, such as (i) low-porosity coatings on fracture walls, and (ii) porosity gradients within the rock matrix. Furthermore, RTD was calculated for the real pore network of the Palmottu granite (Finland). It was shown that the arrangement of spatial heterogeneities located in the immediate vicinity of a conductive fracture modifies the RTD of solutes within the rock matrix. Porous zones located near fractures are of particular importance, because they generate anomalies on the RTD. Notations a Fracture length (m) b Matrix depth (m) D Apparent diffusion coefficient of the pixel (m 2 /s) D 0 Free diffusion coefficient (m 2 /s)

show abstract

Section: Numerical Modeling Of Residence Time Distribution Using the mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Effect of Rock Matrix Heterogeneities Near Fracture Walls on the Residence Time Distribution (RTD) of Solutes

et al. 2007

Self Cite

View full text Add to dashboard Cite

show abstract

“…For these reasons, the results from HYRL1 are less reliable than what was obtained from HYRL2. In order to take into account the complicated geometry of the subsampling procedure in the overcores and time delays, a time domain diffusion (TDD) modelling tool [33][34][35][36] was applied to interpret the HTO and I concentrations obtained from the outleached subsamples. Evaluated diffusion coefficient results are given in detail in companion paper [37].…”

Section: Resultsmentioning

confidence: 99%

The tritiated water and iodine migration in situ in Grimsel granodiorite. Part I: determination of the diffusion profiles

Ikonen

Sardini

Jokelainen

et al. 2016

J Radioanal Nucl Chem

Self Cite

View full text Add to dashboard Cite

show abstract

“…(9) is performed in conventional out-diffusion experiments (Hespe, 1971;Siitari-Kauppi et al, 1997;Sardini et al, 2003). However, using only the M (t) data for the early stages implies that pore structure information from deep inside the cube is discarded, precluding calculation of the diffusion coefficient averaged over the cubic system.…”

Section: Out-diffusion Of Non-sorbing Tracers In Porous Mediamentioning

confidence: 99%

DMAP.m: A Mathematica® program for three-dimensional mapping of tortuosity and porosity of porous media.

Nakashima

Yamaguchi²

2004

Bull. Geol. Surv. Japan

View full text Add to dashboard Cite

(2004) DMAP.m: A Mathematica® program for three-dimensional mapping of tortuosity and porosity of porous media. Bull. Geol. Surv. Japan, vol. 55 (3/ 4), 93-103, 10 figs, 3 tables. Abstract:We developed a Mathematica® program for three-dimensional mapping of the porosity and normalized apparent diffusion coefficient (tortuosity) of isotropic heterogeneous porous media. The program, DMAP.m, is a package-type program for Mathematica® version 4 or later. DMAP.m accepts three-dimensional (3D) digital image data for the porous media as an input. Such data may be obtained by, for example, X-ray computed tomography (CT) as a set of text files of two-dimensional contiguous CT slices (square matrices). DMAP.m reads the text files and divides the image set into sub-cubes, then executes a non-sorbing random walk (lattice walk) through the discrete pore space in each sub-cube. A specified number of voxels are chosen randomly as the start position of the random walk. If the chosen voxel falls within a pore, random walk simulation is carried out until the walker exits the sub-cube. If the chosen voxel falls within a solid, the random walk is not performed. The porosity of each sub-cube is calculated as the probability of a successful hit on a pore voxel in this random choice of the start position (Monte Carlo integral). The time required for the walkers to escape from each sub-cube is recorded in the random walk simulation, representing an "out-diffusion" or "out-leaching" numerical simulation. The tortuosity (apparent diffusion coefficient in the free space divided by that in porous media) is calculated by fitting the time-dependent cumulative number of walkers that have escaped from the sub-cube to a theoretical curve. DMAP.m was applied successfully here to the 3D X-ray CT image of a monosized sand pack. DMAP.m is available for free download on the author's website (URL = http://staff.aist.go.jp/nakashima.yoshito/progeng.htm) to facilitate study on porous media by X-ray CT or nuclear magnetic resonance imaging.

show abstract

Interpretation of out-diffusion experiments on crystalline rocks using random walk modeling

Cited by 34 publications

References 13 publications

The Effect of Rock Matrix Heterogeneities Near Fracture Walls on the Residence Time Distribution (RTD) of Solutes

The Effect of Rock Matrix Heterogeneities Near Fracture Walls on the Residence Time Distribution (RTD) of Solutes

The tritiated water and iodine migration in situ in Grimsel granodiorite. Part I: determination of the diffusion profiles

DMAP.m: A Mathematica® program for three-dimensional mapping of tortuosity and porosity of porous media.

Contact Info

Product

Resources

About