Journal V.24.No.1.2022

Date of publication: 30 Mar 2022
Pages: 2-92
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Editor-in-chief's column

Georesources against the background of external and internal changes

A.V. Sokolov

Editorial

2-2
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Field Development and Operation

Oil production management based on neural network optimization of well operation at the pilot project site of the Vatyeganskoe field (Territorial Production Enterprise Povkhneftegaz)

L.S. Brilliant, M.R. Dulkarnaev, M.Yu. Danko, A.O. Elisheva, D.Kh. Nabiev, A.I. Khutornaya, I.N. Malkov

Original article

DOI https://doi.org/10.18599/grs.2022.1.1

3-15
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Investigation of the wetting effects on two-phase fluid flows in a heterogeneous digital core under dynamic conditions

T.R. Zakirov, M.G. Khramchenkov

Original article

DOI https://doi.org/10.18599/grs.2022.1.2

16-26
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Calculation of the flow rate between wells in the flow model of an oil reservoir using streamlines

K.A. Potashev, R.R. Akhunov, A.B. Mazo 

Original article

DOI https://doi.org/10.18599/grs.2022.1.3

27-35
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Geological and Geochemical Studies. Prospecting and Exploration of Fields

Resource potential of the Chernyshev Swell (the Timan Pechora Basin) in the context of the structure and petroleum habitat of the salt bearing thrust belts

K.O. Sobornov

Original article

DOI https://doi.org/10.18599/grs.2022.1.4

36-50
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Aluminum in quartz of the Darasun gold deposit (Eastern Transbaikal region, Russia)

L.T. Rakov, V.Yu. Prokofiev, E.A. Minervina, L.D. Zorina

Original article

DOI https://doi.org/10.18599/grs.2022.1.5

52-61
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Morphological, textural and structural features of chromitite deposits of Main ore field of Kempirsay massif (South Urals, Kazakhstan)

D.E. Saveliev, D.K. Makatov, V.S. Portnov, R.A. Gataullin

Original article

DOI https://doi.org/10.18599/grs.2022.1.6

62-73
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Physicochemical conditions of the formation of the Larino granite-gneiss dome (South Ural)

V.I. Snachev, A.V. Snachev, V.Yu. Prokofiev

Original article

DOI https://doi.org/10.18599/grs.2022.1.7

74-83
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Geoecological Studies

Digital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learning

K.G. Giniyatullin, I.A. Sahabiev, E.V. Smirnova, I.A. Urazmetov, R.V. Okunev, K.A. Gordeeva 

Original article

DOI https://doi.org/10.18599/grs.2022.1.8

84-92
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