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,
Sergey Stankevich
Affiliations
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
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.
