This study sampled and analyzed water from around the United States. Activities that enhanced this work included the co-collection of rock samples, the statistical analysis of the data with an ESOM, and evaluation of extraction by three promising methods. These activities led to five conclusions (1) REEs sometimes occur in continental groundwater in excess of 1000-times their seawater concentrations. (2) REEs exist as a trace (ng/L) component in all analyzed samples. (3) The traditional distinction of light (Z=57-64) and heavy (Z=65-71) REEs in rock-mineral chemistry applies to aqueous chemistry. (4) REE content is not significantly controlled by lithology, reservoir temperature, nor salinity. (5) REE concentration, both in sampled basins and neural network predictions of potential, appears to be spatially co-associated, with the basin mattering more than formation. These conclusions suggest that future work study three questions. (1) What is the spatially–dependent variable that controls REE concentration? (2) Does the output of the ESOM change significantly as input samples are grown in both number and spatial extent? (3) Compared with next-gen analytical techniques, under what metrics does the current method for aqueous REE analysis perform well, and under what metrics is it surpassed? Some of these questions are being investigated in new projects that grew from this work, others would benefit from government support. The study has five main conclusions. (1) REEs sometimes occur in continental groundwater in excess of 1000-times their seawater concentrations. (2) REEs exist as a trace (ng/L) component in all analyzed samples. (3) The traditional distinction of light and heavy REEs in rock-mineral chemistry applies to aqueous chemistry. The team identifies an unambiguous, and observationally substantiated, break of light and heavy REEs in natural aqueous systems between Gadolinium and Terbium. (4) REE content is not significantly controlled by lithology, reservoir temperature, nor salinity. (5) REE concentration, both in sampled basins and neural network predictions of potential, appears to be spatially co-associated. These five conclusions have advanced the scientific understanding of REEs in subsurface geothermal systems.

Press the Back button in your browser, or search again.

Copyright 2019, Stanford Geothermal Program: Readers who download papers from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the original publisher.