The relation between petrophysical and transport properties of the Boom Clay and Eigenbilzen Sands. (March 2020)

• Record Type: Journal Article
• Authors: Jacops, E.; Rogiers, B.; Frederickx, L.; Swennen, R.; Littke, R.; Krooss, B.M.; Amann-Hildenbrand, A.; Bruggeman, C.
• Is Part Of: Applied geochemistry. Volume 114(2020)
• Publication Date: 2020-03
• Subjects: Boom Clay -- Eigenbilzen Sands -- Diffusion -- Gas -- Hydraulic conductivity -- Correlations -- Predictive modelling; Environmental geochemistry -- Periodicals; Water chemistry -- Periodicals; Geochemistry -- Social aspects -- Periodicals; Geochemistry -- Periodicals; 551.9
• DOI: 10.1016/j.apgeochem.2020.104527
• Languages: English
• ISSNs: 0883-2927

Abstract

Abstract: Within the framework of safe disposal of high and intermediate level radioactive waste, it is well known that for both the Boom Clay and overlying Eigenbilzen Sands (early Oligocene; Rupelian), the composition varies with depth. Though, a detailed understanding of how these intrinsic variations may affect the basic transport properties is still missing. Therefore, a small but comprehensive and unique data set, containing transport parameters (hydraulic conductivity and diffusivity) and related properties (mineral composition and texture, grain size distribution, specific surface area and pore size distribution) of different samples of the Boom Clay and Eigenbilzen Sands has been gathered during recent years. First, the variability in transport parameters and petrophysical properties is discussed and mutual relationships are investigated. Second, the performance of predictive modelling is explored. The multivariate analysis clearly illustrates that the data are mainly explained by two main axes of variation. One corresponds to the logarithmic hydraulic conductivity (logK) and all petrophysical properties that are very strongly correlated to it. The second axis is independent of logK, and relates to the size of the diffusing molecules. Finally, predictive modelling seems promising at this point, but the limited quality of the currently available test cases prohibits being conclusive on the model performance and its generalisability. Highlights: A unique data set with transport parameters and petrophyiscal properties is presented. Variability within the data set is discussed. Mutual relationships are investigated. The performance of predictive modelling is explored.