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Assessment of NMR Logging for Estimating Hydraulic Conductivity in Glacial Aquifers
Author(s) -
Kendrick Alexander K.,
Knight Rosemary,
Johnson Carole D.,
Liu Gaisheng,
Knobbe Steven,
Hunt Randall J.,
Butler James J.
Publication year - 2020
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/gwat.13014
Subject(s) - hydraulic conductivity , aquifer , calibration , permeameter , well logging , glacial period , geology , groundwater , environmental science , magnetization , soil science , magnetic field , physics , soil water , geophysics , geotechnical engineering , geomorphology , quantum mechanics
Glacial aquifers are an important source of groundwater in the United States and require accurate characterization to make informed management decisions. One parameter that is crucial for understanding the movement of groundwater is hydraulic conductivity, K . Nuclear magnetic resonance (NMR) logging measures the NMR response associated with the water in geological materials. By utilizing an external magnetic field to manipulate the nuclear spins associated with 1 H, the time‐varying decay of the nuclear magnetization is measured. This logging method could provide an effective way to estimate K at submeter vertical resolution, but the models that relate NMR measurements to K require calibration. At two field sites in a glacial aquifer in central Wisconsin, we collected a total of four NMR logs and obtained measurements of K in their immediate vicinity with a direct‐push permeameter (DPP). Using a bootstrap algorithm to calibrate the Schlumberger‐Doll Research (SDR) NMR‐ K model, we estimated K to within a factor of 5 of the DPP measurements. The lowest levels of accuracy occurred in the lower‐ K ( K < 10 −4 m/s) intervals. We also evaluated the applicability of prior SDR model calibrations. We found the NMR calibration parameters varied with K , suggesting the SDR model does not incorporate all the properties of the pore space that control K . Thus, the expected range of K in an aquifer may need to be considered during calibration of NMR‐ K models. This study is the first step toward establishing NMR logging as an effective method for estimating K in glacial aquifers.