Forecast of lithium concentrations in industrial brines based on the experience of long-term field operation under conditions of a shortage of initial data

UDK: 541.1:622.276

DOI: 10.24887/0028-2448-2025-11-108-113

Key words: industrial brines, associated waters, hydromineral raw materials, Rifian aquifer complex, lithium, rubidium, strontium, machine learning, random forest, correlation analysis

Authors: A.A. Ishkov (RN-KrasnoyarskNIPIneft LLC, RF, Krasnoyarsk; Industrial University of Tyumen, RF, Tyumen); M.S. Zarubov (RN-KrasnoyarskNIPIneft LLC, RF, Krasnoyarsk); M.A. Danilova (RN-KrasnoyarskNIPIneft LLC, RF, Krasnoyarsk; Irkutsk State Technical University, RF, Irkutsk); A.V. Korzun1,4 (RN-KrasnoyarskNIPIneft LLC, RF, Krasnoyarsk; Lomonosov Moscow State University, RF, Moscow); A.P. Gorokhov (RN-KrasnoyarskNIPIneft LLC, RF, Krasnoyarsk)

This study aims to develop and test a method for predicting the stability of valuable microcomponent concentrations (Li+, Sr2+, Br-) in industrial brines extracted alongside hydrocarbons during the development of oil fields on the Siberian Platform. A comprehensive analysis of a set of hydrogeochemical data obtained from 201 wells in the Kuyumbinskoye field was collected and performed. The database includes 450 chemical analysis reports for water macrocomponents and 211 reports for microcomponents. For the first time, a comprehensive approach was applied, combining traditional correlation analysis and modern machine learning methods (the Random Forest algorithm), which enables predicting the concentrations of valuable microcomponents based on regularly measured macrocomponents as part of the analysis of produced water from the operating fund. Stable correlations between Li+ and Ca2+, Mg2+, Na+, K+, pH, and mineralization were identified. The developed predictive model enables a high degree of reliability (coefficient of determination was equal to 0,93 on the training sample and 0,77 on the test sample) to assess the distribution of lithium concentrations across wells and predict their dynamics during long-term operation of the deposit, including during periods when there is no established monitoring system. It was established that in most wells there is no significant decrease in lithium concentrations during many years of production, which indicates the stability of the hydrogeochemical conditions of the Rifian aquifer complex. The proposed methodology significantly reduces the cost of assessing the resource potential of hydromineral raw materials and can be adapted for other regions and components.

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