SATELLITE GRAVIMETRY AS A TOOL FOR FORECASTING OIL AND GAS POTENTIAL

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.

satellite gravimetry, oil and gas content, hydrocarbon deposits, gravity field, hydrocarbon exploration, heat flow, machine learning, logistic regression