Predicting Rare Earth Element Potential in Produced and Geothermal Waters of the United States via Emergent Self-Organizing Maps

Mark A. Engle, Charles W. Nye, Ghanashyam Neupane, Scott A. Quillinan, Jonathan Fred McLaughlin, Travis McLing and Josep A. Martín-Fernández
Additional contact information
Mark A. Engle: Department of Earth, Environmental and Resource Sciences, University of Texas at El Paso, 500 West University Ave., El Paso, TX 79930, USA
Charles W. Nye: Center for Economic Geology Research, University of Wyoming, Laramie, WY 82071, USA
Ghanashyam Neupane: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Scott A. Quillinan: Center for Economic Geology Research, University of Wyoming, Laramie, WY 82071, USA
Jonathan Fred McLaughlin: Center for Economic Geology Research, University of Wyoming, Laramie, WY 82071, USA
Travis McLing: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Josep A. Martín-Fernández: Department of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003 Girona, Spain

Energies, 2022, vol. 15, issue 13, 1-21

Abstract: This work applies emergent self-organizing map (ESOM) techniques, a form of machine learning, in the multidimensional interpretation and prediction of rare earth element (REE) abundance in produced and geothermal waters in the United States. Visualization of the variables in the ESOM trained using the input data shows that each REE, with the exception of Eu, follows the same distribution patterns and that no single parameter appears to control their distribution. Cross-validation, using a random subsample of the starting data and only using major ions, shows that predictions are generally accurate to within an order of magnitude. Using the same approach, an abridged version of the U.S. Geological Survey Produced Waters Database, Version 2.3 (which includes both data from produced and geothermal waters) was mapped to the ESOM and predicted values were generated for samples that contained enough variables to be effectively mapped. Results show that in general, produced and geothermal waters are predicted to be enriched in REEs by an order of magnitude or more relative to seawater, with maximum predicted enrichments in excess of 1000-fold. Cartographic mapping of the resulting predictions indicates that maximum REE concentrations exceed values in seawater across the majority of geologic basins investigated and that REEs are typically spatially co-associated. The factors causing this co-association were not determined from ESOM analysis, but based on the information currently available, REE content in produced and geothermal waters is not directly controlled by lithology, reservoir temperature, or salinity.

Keywords: emergent self-organizing maps; Critical Minerals; compositional data analysis; neural networks; brines (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/13/4555/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/13/4555/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:13:p:4555-:d:844802

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().