Carole D. Johnson | Search USGS

USGS2025

Fiber-optic distributed temperature sensing of hydrologic processes—Diverse deployments and new applications by the U.S. Geological Survey

Fiber-optic distributed temperature sensing instruments harness the temperature-dependent properties of glass to measure temperature continuously along optical fibers by using precise pulses of laser light. In the mid-2000s, this technology was refined for environmental monitoring purposes such as snowpack-air exchange, groundwater/surface-water exchange, and lake-water stratification. Fiber-optic distributed temperature sensing has revealed unprecedented details about preferential flow processes; however, the method is labor intensive and requires specific training, resulting in limited use by the broader water community. With the ongoing national implementation of the U.S. Geological Survey Next Generation Water Observing System, there has been renewed interest in harnessing the unique spatiotemporal monitoring capabilities of fiber-optic distributed temperature sensing. This fact sheet briefly describes this technology, highlights uses by the U.S. Geological Survey, and discusses current applications and future opportunities.

Fact Sheet↗

USGS2023

Evaluation of models for estimating hydraulic conductivity in glacial aquifers from NMR logging

Nuclear magnetic resonance (NMR) logging is a promising method for estimating hydraulic conductivity ( K ). During the past ∼60 years, NMR logging has been used for petroleum applications, and different models have been developed for deriving estimates of permeability. These models involve calibration parameters whose values were determined through decades of research on sandstones and carbonates. We assessed the use of five models to derive estimates of K in glacial aquifers from NMR logging data acquired in two wells at each of two field sites in central Wisconsin, USA. Measurements of K , obtained with a direct push permeameter (DPP), K DPP , were used to obtain the calibration parameters in the Schlumberger-Doll Research, Seevers, Timur-Coates, Kozeny-Godefroy, and sum-of-echoes (SOE) models so as to predict K from the NMR data; and were also used to assess the ability of the models to predict K DPP . We obtained four well-scale calibration parameter values for each model using the NMR and DPP measurements in each well; and one study-scale parameter value for each model by using all data. The SOE model achieved an agreement with K DPP that matched or exceeded that of the other models. The Timur-Coates estimates of K were found to be substantially different from K DPP . Although the well-scale parameter values for the Schlumberger-Doll, Seevers, and SOE models were found to vary by less than a factor of 2, more research is needed to confirm their general applicability so that site-specific calibration is not required to obtain accurate estimates of K from NMR logging data.

Wisconsin↗