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000856717 1001_ $$00000-0002-3279-5202$$aBusch, A.$$b0$$eCorresponding author
000856717 245__ $$aPredicting Effective Diffusion Coefficients in Mudrocks Using a Fractal Model and Small-Angle Neutron Scattering Measurements
000856717 260__ $$a[New York]$$bWiley$$c2018
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000856717 520__ $$aThe determination of effective diffusion coefficients of gases or solutes in the water-saturated pore space of mudrocks is time consuming and technically challenging. Yet, reliable values of effective diffusion coefficients are important to predict migration of hydrocarbon gases in unconventional reservoirs, dissipation of (explosive) gases through clay barriers in radioactive waste repositories, mineral alteration of seals to geological CO2 storage reservoirs and contaminant migration through aquitards.  In this study, small angle and very small angle neutron scattering techniques have been utilized to determine a range of transport properties in mudrocks, including porosity, pore size distributions and surface and volume fractal dimensions of pores and grains, from which diffusive transport parameters can be estimated. Using a fractal model derived from Archie’s Law, we calculate effective diffusion coefficients from these parameters and compare them to laboratory-derived effective diffusion coefficients for CO2, H2, CH4 and HTO on either the same or related mudrock samples. The samples include Opalinus Shale from the underground laboratory in Mont Terri, Switzerland; Boom Clay from a core drilled in Mol, Belgium and a marine claystone cored in Utah, USA. The predicted values were compared to laboratory diffusion measurements. The measured and modelled diffusion coefficients show good agreement, differing generally by less than factor 5. Neutron or X-ray scattering analysis is therefore proposed as a novel method for fast, accurate estimation of effective diffusion coefficients in mudrocks, together with simultaneous measurement of multiple transport parameters including porosity, pore size distributions and surface areas, important for (reactive) transport modelling.
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000856717 7001_ $$0P:(DE-HGF)0$$aKampman, N.$$b1
000856717 7001_ $$00000-0003-1770-412X$$aBertier, P.$$b2
000856717 7001_ $$0P:(DE-Juel1)130893$$aPipich, V.$$b3$$ufzj
000856717 7001_ $$0P:(DE-Juel1)144382$$aFeoktystov, A.$$b4$$ufzj
000856717 7001_ $$00000-0003-4921-6294$$aRother, G.$$b5
000856717 7001_ $$00000-0001-6867-7147$$aHarrington, J.$$b6
000856717 7001_ $$0P:(DE-HGF)0$$aLeu, L.$$b7
000856717 7001_ $$0P:(DE-HGF)0$$aAertens, M.$$b8
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000856717 773__ $$0PERI:(DE-600)2029553-4$$a10.1029/2018WR023425$$gVol. 54, no. 9, p. 7076 - 7091$$n9$$p7076 - 7091$$tWater resources research$$v54$$x0043-1397$$y2018
000856717 8564_ $$uhttps://juser.fz-juelich.de/record/856717/files/Busch_et_al-2018-Water_Resources_Research-1.pdf$$yPublished on 2018-09-25. Available in OpenAccess from 2019-03-25.
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