Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods

Approximately 50% of the world’s uranium is mined in a closed way using underground well leaching. In the process of uranium mining at formation-infiltration deposits, an important role is played by the correct identification of the formation of reservoir oxidation zones (ROZs), within which the ura...

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Published in:Mathematics (Basel) 2023-11, Vol.11 (22), p.4687
Main Authors: Mukhamediev, Ravil I., Kuchin, Yan, Popova, Yelena, Yunicheva, Nadiya, Muhamedijeva, Elena, Symagulov, Adilkhan, Abramov, Kirill, Gopejenko, Viktors, Levashenko, Vitaly, Zaitseva, Elena, Litvishko, Natalya, Stankevich, Sergey
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Classification
Data logging
Deposits
ensemble machine learning
Equilibrium
Gamma rays
Infiltration
Leaching
Lithology
Machine learning
Methods
Neural networks
Oil fields
Oxidation
Oxidation-reduction reaction
Permeability
Research methodology
reservoir oxidation zone
Reservoirs
Skilled labor
Stratigraphy
Support vector machines
Uranium
uranium mining
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Summary:Approximately 50% of the world’s uranium is mined in a closed way using underground well leaching. In the process of uranium mining at formation-infiltration deposits, an important role is played by the correct identification of the formation of reservoir oxidation zones (ROZs), within which the uranium content is extremely low and which affect the determination of ore reserves and subsequent mining processes. The currently used methodology for identifying ROZs requires the use of highly skilled labor and resource-intensive studies using neutron fission logging; therefore, it is not always performed. At the same time, the available electrical logging measurements data collected in the process of geophysical well surveys and exploration well data can be effectively used to identify ROZs using machine learning models. This study presents a solution to the problem of detecting ROZs in uranium deposits using ensemble machine learning methods. This method provides an index of weighted harmonic measure (f1_weighted) in the range from 0.72 to 0.93 (XGB classifier), and sufficient stability at different ratios of objects in the input dataset. The obtained results demonstrate the potential for practical use of this method for detecting ROZs in formation-infiltration uranium deposits using ensemble machine learning.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11224687