California's Statewide AEM Surveys: Project Implementation and Next Steps

<p>Passage of the Sustainable Groundwater Management ACT (SGMA) in California has resulted in the need for improving the understanding of groundwater aquifers to support groundwater managers in developing and implementing groundwater management plans and actions. The California Department of Water Resources (DWR) has supported this effort by implementing the Statewide AEM Survey Project, where data are collected in a reconnaissance grid across California's priority basins. Raw, processed, inverted, and interpreted AEM data as well as digitized lithology and e-logs are made publicly available and novel tools have been developed to support data accessibility. With the Statewide AEM Surveys nearing completion, DWR is undertaking an effort to utilize the Statewide AEM Survey dataset along with other existing data (surface geophysics, lithology logs, elogs, geologic cross sections) to provide an improved understanding of basin characteristics. To support this task, new tools are being developed that will analyse all data available to produce refined, texture and hydrogeologic models. Results will be archived in DWR's California Groundwater publication and Basin Reports and models will be available to visualize through new and innovative 3D, GIS-based tools. To support this effort, DWR will also be conducting Pilot Studies that will include the collection of additional data with the goal of filling data gaps and addressing specific SGMA implementation questions. The first Pilot Study will be conducted on the eastern side of the San Joaquin Valley in California's Central Valley and will include the collection of infill AEM data, as well as other ground-based geophysical surveys.</p>Open-Access Online Publication: October 30, 202

Field experiment provides ground truth for surface nuclear magnetic resonance measurement

The need for sustainable management of fresh water resources is one of the great challenges of the 21st century. Since most of the planet’s liquid fresh water exists as groundwater, it is essential to develop non-invasive geophysical techniques to characterize groundwater aquifers. A field experiment was conducted in the High Plains Aquifer, central United States, to explore the mechanisms governing the non-invasive Surface NMR (SNMR) technology. We acquired both SNMR data and logging NMR data at a field site, along with lithology information from drill cuttings. This allowed us to directly compare the NMR relaxation parameter measured during logging, T2, to the relaxation parameter T2* measured using the SNMR method. The latter can be affected by inhomogeneity in the magnetic field, thus obscuring the link between the NMR relaxation parameter and the hydraulic conductivity of the geologic material. When the logging T2 data were transformed to pseudo- T2* data, by accounting for inhomogeneity in the magnetic field and instrument dead time, we found good agreement with T2* obtained from the SNMR measurement. These results, combined with the additional information about lithology at the site, allowed us to delineate the physical mechanisms governing the SNMR measurement. Such understanding is a critical step in developing SNMR as a reliable geophysical method for the assessment of groundwater resources

Use of NMR logging to obtain estimates of hydraulic conductivity in the High Plains aquifer, Nebraska, USA

Hydraulic conductivity (K) is one of the most important parameters of interest in groundwater applications because it quantifies the ease with which water can flow through an aquifer material. Hydraulic conductivity is typically measured by conducting aquifer tests or wellbore flow (WBF) logging. Of interest in our research is the use of proton nuclear magnetic resonance (NMR) logging to obtain information about water-filled porosity and pore space geometry, the combination of which can be used to estimate K. In this study, we acquired a suite of advanced geophysical logs, aquifer tests, WBF logs, and sidewall cores at the field site in Lexington, Nebraska, which is underlain by the High Plains aquifer. We first used two empirical equations developed for petroleum applications to predict K from NMR logging data: the Schlumberger Doll Research equation (KSDR) and the Timur-Coates equation (KT-C), with the standard empirical constants determined for consolidated materials. We upscaled our NMR-derived K estimates to the scale of the WBF-logging K(KWBF-logging) estimates for comparison. All the upscaled KT-C estimates were within an order of magnitude of KWBF-logging and all of the upscaled KSDR estimates were within 2 orders of magnitude of KWBF-logging. We optimized the fit between the upscaled NMR-derived K and KWBF-logging estimates to determine a set of site-specific empirical constants for the unconsolidated materials at our field site. We conclude that reliable estimates of K can be obtained from NMR logging data, thus providing an alternate method for obtaining estimates of K at high levels of vertical resolution