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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| Format: | Article |
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MDPI AG
2023-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/22/4687 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Ravil I. Mukhamediev |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:37:47Z |
| format | Article |
| id | doaj.art-04fe2738de12462fbcbfe995ee78bc4b |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T16:37:47Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-04fe2738de12462fbcbfe995ee78bc4b2023-11-24T14:54:29ZengMDPI AGMathematics2227-73902023-11-011122468710.3390/math11224687Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning MethodsRavil I. Mukhamediev0Yan Kuchin1Yelena Popova2Nadiya Yunicheva3Elena Muhamedijeva4Adilkhan Symagulov5Kirill Abramov6Viktors Gopejenko7Vitaly Levashenko8Elena Zaitseva9Natalya Litvishko10Sergey Stankevich11Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, KazakhstanInstitute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, KazakhstanTransport and Management Faculty, Transport and Telecommunication Institute, 1 Lomonosov Str., LV-1019 Riga, LatviaInstitute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str., Almaty 050010, KazakhstanInstitute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str., Almaty 050010, KazakhstanInstitute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty 050013, KazakhstanInstitute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str., Almaty 050010, KazakhstanInternational Radio Astronomy Centre, Ventspils University of Applied Sciences, LV-3601 Ventspils, LatviaFaculty of Management Science and Informatics, University of Zilina, 010 26 Žilina, SlovakiaFaculty of Management Science and Informatics, University of Zilina, 010 26 Žilina, SlovakiaInstitute of Information and Computational Technologies CS MSHE RK, 28 Shevchenko Str., Almaty 050010, KazakhstanScientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, 01054 Kyiv, UkraineApproximately 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.https://www.mdpi.com/2227-7390/11/22/4687uranium miningmachine learningreservoir oxidation zoneensemble machine learning |
| spellingShingle | 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 Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods Mathematics uranium mining machine learning reservoir oxidation zone ensemble machine learning |
| title | Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods |
| title_full | Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods |
| title_fullStr | Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods |
| title_full_unstemmed | Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods |
| title_short | Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods |
| title_sort | determination of reservoir oxidation zone formation in uranium wells using ensemble machine learning methods |
| topic | uranium mining machine learning reservoir oxidation zone ensemble machine learning |
| url | https://www.mdpi.com/2227-7390/11/22/4687 |
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