Fractal geometry of rocks

The analysis of small- and ultra-small-angle neutron scattering data for sedimentary rocks shows that the pore-rock fabric interface is a surface fractal (Ds = 2.82) over 3 orders of magnitude of the length scale and 10 orders of magnitude in intensity. The fractal dimension and scatterer size obtained from scanning electron microscopy image processing are consistent with neutron scattering data

Predicting effective diffusion coefficients in mudrocks using a fractal model and small-angle neutron scattering measurements

The 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 modeled 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 modeling

Robust ordinal regression in preference learning and ranking

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking

Pore accessibility by methane and carbon dioxide in coal as determined by neutron scattering

Contrast-matching ultrasmall-angle neutron scattering (USANS) and small-angle neutron scattering (SANS) techniques were used for the first time to determine both the total pore volume and the fraction of the pore volume that is inaccessible to deuterated methane, CD4, in four bituminous coals in the range of pore sizes between ∼10 Å and ∼5 μm. Two samples originated from the Illinois Basin in the U.S.A., and the other two samples were commercial Australian bituminous coals from the Bowen Basin. The total and inaccessible porosity were determined in each coal using both Porod invariant and the polydisperse spherical particle (PDSP) model analysis of the scattering data acquired from coals both in vacuum and at the pressure of CD4, at which the scattering length density of the pore-saturating fluid is equal to that of the solid coal matrix (zero average contrast pressure). The total porosity of the coals studied ranged from 7 to 13%, and the volume of pores inaccessible to CD4 varied from ∼13 to ∼36% of the total pore volume. The volume fraction of inaccessible pores shows no correlation with the maceral composition; however, it increases with a decreasing total pore volume. In situ measurements of the structure of one coal saturated with CO2 and CD4 were conducted as a function of the pressure in the range of 1−400 bar. The neutron scattering intensity from small pores with radii less than 35 Å in this coal increased sharply immediately after the fluid injection for both gases, which demonstrates strong condensation and densification of the invading subcritical CO2 and supercritical methane in small pores

Assessing the potential for CO2 adsorption in a subbituminous coal, Huntly Coalfield, New Zealand, using small angle scattering techniques

Small angle scattering techniques (SAXS and SANS) have been used to investigate the microstructural properties of the subbituminous coals (R-max 0.42-0.45%) from the Huntly Coalfield, New Zealand. Samples were collected from the two thick (>5 m) coal seams in the coalfield and have been analysed for methane and carbon dioxide sorption capacity, petrography, pore size distribution, specific surface area and porosity