Conférence et webinaire: «High-resolution Characterization of Hydraulic Conductivity Using Direct-Push Tools»

Série de conférences en hydrogéologie

Bonjour,

Notre prochaine conférence, « High-resolution Characterization of Hydraulic Conductivity Using Direct-Push Tools », sera présentée par M. Geoffrey C. Bohling de l'Université du Kansas. La présentation sera diffusée en anglais par vidéoconférence à partir de l'INRS, le mercredi 2 avril 2014 à 12h15.

Pour y assister, 3 options s'offrent à vous:

               1- soit sur place, à l'INRS, local 2301

               2- soit sur place, à l'UQAM, local PK-6120

3- soit depuis votre ordinateur personnel via la plateforme webinaire Adobe connect, en vous inscrivant à notre liste d'envois à gries.coord@gmail.com

Notez que l'option no 3 nécessite l'installation préalable de Meeting Add-ins for Adobe Connect 9 dans votre ordinateur, que vous pouvez télécharger ici. Adobe connect est aussi incompatible avec le navigateur Google Chrome.

Résumé: 

Spatial variations in hydraulic conductivity (K) provide critical controls on solute transport in the subsurface. Direct-push tools have been developed for high-resolution characterization of K variations in unconsolidated settings. The direct-push injection logger (DPIL) provides an indicator (flux/pressure, Q/P) of relative variations in K at a very high vertical resolution. The more recently developed HRK (High-Resolution K) tool combines the DPIL with a direct-push permeameter (DPP), providing a means to transform the DPIL Q/P profiles into high-resolution K profiles. These tools were applied to obtain 58 K profiles with a vertical sample spacing of 1.5 cm from the heavily studied macrodispersion experiment (MADE) site. We have compared the data from these 58 profiles with those from the 67 flowmeter profiles that have served as the primary basis for characterizing the heterogeneous aquifer at the site. Overall, the patterns of variation displayed by the two data sets are quite similar, in terms of both large-scale structure and autocorrelation characteristics, although the two datasets exhibit distinct differences in geometric mean K and lnK variance. The DPIL calibration approach used in the foregoing work has some drawbacks. There is an upper limit on accurate Q/P values due to a lower limit on measureable DPIL pressure responses in high-K zones. Our previous DPIL calibration approach assumed a linear relationship between lnK and ln(Q/P); consequently, the Q/P limit was translated into an upper threshold on accurate DPIL-based K estimates. Current work aims to improve the calibration by incorporating an adaptive, nonlinear ln(Q/P) vs. lnK relationship into the inversion process, allowing the estimated K profiles to more accurately reflect the very high K's indicated by some of the DPP tests.

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