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

Ravil I. Mukhamediev, Yan Kuchin (), Yelena Popova, Nadiya Yunicheva (), Elena Muhamedijeva, Adilkhan Symagulov, Kirill Abramov, Viktors Gopejenko, Vitaly Levashenko, Elena Zaitseva, Natalya Litvishko and Sergey Stankevich
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Ravil I. Mukhamediev: Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan
Yan Kuchin: Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan
Yelena Popova: Transport and Management Faculty, Transport and Telecommunication Institute, 1 Lomonosov Str., LV-1019 Riga, Latvia
Nadiya Yunicheva: Institute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str., Almaty 050010, Kazakhstan
Elena Muhamedijeva: Institute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str., Almaty 050010, Kazakhstan
Adilkhan Symagulov: Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan
Kirill Abramov: Institute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str., Almaty 050010, Kazakhstan
Viktors Gopejenko: International Radio Astronomy Centre, Ventspils University of Applied Sciences, LV-3601 Ventspils, Latvia
Vitaly Levashenko: Faculty of Management Science and Informatics, University of Zilina, 010 26 Žilina, Slovakia
Elena Zaitseva: Faculty of Management Science and Informatics, University of Zilina, 010 26 Žilina, Slovakia
Natalya Litvishko: Institute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str., Almaty 050010, Kazakhstan
Sergey Stankevich: Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, 01054 Kyiv, Ukraine

Mathematics, 2023, vol. 11, issue 22, 1-20

Abstract: 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.

Keywords: uranium mining; machine learning; reservoir oxidation zone; ensemble machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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