from 01.01.2017 until now
Kazan, Kazan, Russian Federation
from 01.01.2008 until now
Kazan, Russian Federation
Russian Federation
from 01.01.2021 until now
Russian Federation
Russian Federation
VAK Russia 1.6
UDC 550.312
UDC 550.831.016
UDC 550.8
UDC 55
UDC 550.34
UDC 550.383
CSCSTI 37.31
CSCSTI 37.01
CSCSTI 37.15
CSCSTI 37.25
CSCSTI 38.01
CSCSTI 36.00
CSCSTI 37.00
CSCSTI 38.00
CSCSTI 39.00
CSCSTI 52.00
Russian Classification of Professions by Education 05.06.01
Russian Library and Bibliographic Classification 263
Russian Library and Bibliographic Classification 26
Russian Trade and Bibliographic Classification 6320
Russian Trade and Bibliographic Classification 63
BISAC SCI032000 Physics / Geophysics
BISAC SCI SCIENCE
This study explores the use of satellite gravity data and derived crustal models for predicting oil and gas potential in the east of the Russian platform. The research utilizes structural data (including GOCE satellite gravity-derived Moho depth), thermal data, and hydrocarbon potential data. The methodology involves three steps: 1) statistical analysis using Student's 𝑡-test to identify significant parameters distinguishing areas with and without hydrocarbon fields; 2) classification of the study area into three zones based on their hydrocarbon potential; and 3) application of a logistic regression machine learning model to forecast hydrocarbon potential in uncertain areas. The results show that most analyzed parameters have statistically significant differences between areas with and without hydrocarbon fields. The logistic regression model achieves 83% accuracy in predicting hydrocarbon potential. The study concludes that satellite gravity data and derived crustal models can be effectively used to forecast oil and gas potential in sedimentary basins, with the Precaspian basin, Cis-Ural trough, parts of the Central-Russia and Mezen rift systems, and the Timan-Pechora basin identified as the most promising areas in the east of the Russian platform.
satellite gravimetry, oil and gas content, hydrocarbon deposits, gravity field, hydrocarbon exploration, heat flow, machine learning, logistic regression
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