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

Mukhamediev R.I. Kuchin Y. Popova Y. Yunicheva N. Muhamedijeva E. Symagulov A. Abramov K. Gopejenko V. Levashenko V. Zaitseva E. Litvishko N. Stankevich S.
November 2023 Multidisciplinary Digital Publishing Institute (MDPI)

Mathematics
2023 #11 Issue 22

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.

ensemble machine learning , machine learning , reservoir oxidation zone , uranium mining

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Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty, 050013, Kazakhstan
Institute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str, Almaty, 050010, Kazakhstan
Transport and Management Faculty, Transport and Telecommunication Institute, 1 Lomonosov Str, Riga, LV-1019, Latvia
Institute of Automation and Information Technologies, Almaty University of Energy and Communications, Baitursynov Str., 126/1, Almaty, 050013, Kazakhstan
International Radio Astronomy Centre, Ventspils University of Applied Sciences, Ventspils, LV-3601, Latvia
Department of Natural Science and Computer Technologies, ISMA University of Applied Sciences, Riga, LV-1019, Latvia
Faculty of Management Science and Informatics, University of Zilina, Žilina, 010 26, Slovakia
Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Kyiv, 01054, Ukraine

Institute of Automation and Information Technologies
Institute of Information and Computational Technologies CS MSHE RK
Transport and Management Faculty
Institute of Automation and Information Technologies
International Radio Astronomy Centre
Department of Natural Science and Computer Technologies
Faculty of Management Science and Informatics
Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine

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