| Form of presentation | Articles in Russian journals and collections |
| Year of publication | 2025 |
| Язык | английский |
|
Garaev Fagim Nazipovich, author
Mulikova Dinara Ilkhomovna, author
Nurgaliev Danis Karlovich, author
Khamidullina Galina Suleymanovna, author
|
|
Ikhsanova Diana Ildarovna, postgraduate kfu
|
| Bibliographic description in the original language |
Ognev I., Khamidullina G., Nourgaliev D., Garaev F., Ikhsanova D., Mulikova D. Satellite Gravimetry as a Tool for Forecasting Oil and Gas Potential // Russ. J. Earth Sci. 2025. P. 1–5. |
| Annotation |
Russian Journal of Earth Sciences |
| Keywords |
satellite gravimetry, oil and gas content, hydrocarbon deposits, gravity field, hydrocarbon exploration, heat flow, machine learning, logistic regression |
| The name of the journal |
Russian Journal of Earth Sciences
|
| URL |
https://journals.rcsi.science/1681-1208/article/view/337464 |
| Please use this ID to quote from or refer to the card |
https://repository.kpfu.ru/eng/?p_id=318341&p_lang=2 |
| Resource files | |
|
|
Full metadata record  |
| Field DC |
Value |
Language |
| dc.contributor.author |
Garaev Fagim Nazipovich |
ru_RU |
| dc.contributor.author |
Mulikova Dinara Ilkhomovna |
ru_RU |
| dc.contributor.author |
Nurgaliev Danis Karlovich |
ru_RU |
| dc.contributor.author |
Khamidullina Galina Suleymanovna |
ru_RU |
| dc.contributor.author |
Ikhsanova Diana Ildarovna |
ru_RU |
| dc.date.accessioned |
2025-01-01T00:00:00Z |
ru_RU |
| dc.date.available |
2025-01-01T00:00:00Z |
ru_RU |
| dc.date.issued |
2025 |
ru_RU |
| dc.identifier.citation |
Ognev I., Khamidullina G., Nourgaliev D., Garaev F., Ikhsanova D., Mulikova D. Satellite Gravimetry as a Tool for Forecasting Oil and Gas Potential // Russ. J. Earth Sci. 2025. P. 1–5. |
ru_RU |
| dc.identifier.uri |
https://repository.kpfu.ru/eng/?p_id=318341&p_lang=2 |
ru_RU |
| dc.description.abstract |
Russian Journal of Earth Sciences |
ru_RU |
| dc.description.abstract |
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 -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. |
ru_RU |
| dc.language.iso |
ru |
ru_RU |
| dc.subject |
satellite gravimetry |
ru_RU |
| dc.subject |
oil and gas content |
ru_RU |
| dc.subject |
hydrocarbon deposits |
ru_RU |
| dc.subject |
gravity field |
ru_RU |
| dc.subject |
hydrocarbon exploration |
ru_RU |
| dc.subject |
heat flow |
ru_RU |
| dc.subject |
machine learning |
ru_RU |
| dc.subject |
logistic regression |
ru_RU |
| dc.title |
Satellite Gravimetry as a Tool for Forecasting Oil and Gas Potential |
ru_RU |
| dc.type |
Articles in Russian journals and collections |
ru_RU |
|