Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

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

Journal Article · · Energies
DOI:https://doi.org/10.3390/en15134555· OSTI ID:1873575
; ; ; ; ; ;

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.

Sponsoring Organization:
USDOE
Grant/Contract Number:
EE0007603
OSTI ID:
1873575
Journal Information:
Energies, Journal Name: Energies Journal Issue: 13 Vol. 15; ISSN 1996-1073; ISSN ENERGA
Publisher:
MDPI AGCopyright Statement
Country of Publication:
Switzerland
Language:
English

References (26)

Advances in self-organizing maps for their application to compositional data journal February 2019
Visualization of Volcanic Rock Geochemical Data and Classification with Artificial Neural Networks journal August 2006
Hunting for Geochemical Associations of Elements: Factor Analysis and Self-Organising Maps journal June 2010
Interpretation of Na–Cl–Br Systematics in Sedimentary Basin Brines: Comparison of Concentration, Element Ratio, and Isometric Log-ratio Approaches journal December 2012
Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods journal May 2017
Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry journal September 2021
Rare earth elements in chloride-rich groundwater, Palo Duro Basin, Texas, USA journal April 1992
The rare earth element geochemistry of acid-sulphate and acid-sulphate-chloride geothermal systems from Yellowstone National Park, Wyoming, USA journal February 1997
Rare earth elements in seawater: particle association, shale-normalization, and Ce oxidation journal February 1999
Considerations in the application of machine learning to aqueous geochemistry: Origin of produced waters in the northern U.S. Gulf Coast Basin journal December 2019
Interpretation of multivariate outliers for compositional data journal February 2012
Geochemistry of formation waters from the Wolfcamp and “Cline” shales: Insights into brine origin, reservoir connectivity, and fluid flow in the Permian Basin, USA journal May 2016
Hydrothermal transport, deposition, and fractionation of the REE: Experimental data and thermodynamic calculations journal November 2016
zCompositions — R package for multivariate imputation of left-censored data under a compositional approach journal April 2015
Geochemical evolution of produced waters from hydraulic fracturing of the Marcellus Shale, northern Appalachian Basin: A multivariate compositional data analysis approach journal June 2014
Imputation of missing values for compositional data using classical and robust methods journal December 2010
Kohonen neural network and factor analysis based approach to geochemical data pattern recognition journal October 2009
Linking compositional data analysis with thermodynamic geochemical modeling: Oilfield brines from the Permian Basin, USA journal June 2014
Analysis and prediction of produced water quantity and quality in the Permian Basin using machine learning techniques journal December 2021
Discovering geochemical patterns using self-organizing neural networks: a new perspective for sedimentary provenance analysis journal March 2004
Rare Earth Elements Occurrence and Economical Recovery Strategy from Shale Gas Wastewater in the Sichuan Basin, China journal July 2020
Dealing with Zeros and Missing Values in Compositional Data Sets Using Nonparametric Imputation journal April 2003
Patterns of ocean current variability on the West Florida Shelf using the self-organizing map journal January 2005
The tremendous potential of deep-sea mud as a source of rare-earth elements journal April 2018
An evaluation of methods for imputation of missing trace element data in groundwaters journal April 2007
Machine learning deciphers CO2 sequestration and subsurface flowpaths from stream chemistry journal January 2021

Similar Records

Related Subjects