2025, Vol. 12, No. 4. - go to content...

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DOI: 10.15862/33NZOR425 (https://doi.org/10.15862/33NZOR425)

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Kapustina O.A. Geoinformation method for assessing the impact of mineral deposit development on the seismic regime of a techno-natural system. Russian journal of resources, conservation and recycling. 2025; 12(4). Available at: https://resources.today/PDF/33NZOR425.pdf (in Russian). DOI: 10.15862/33NZOR425

Geoinformation method for assessing the impact of mineral deposit development on the seismic regime of a techno-natural system

Kapustina Oksana Aleksandrovna
Orenburg Federal Research Center of the Ural Branch of the Russian Academy of Sciences, Orenburg, Russia
E-mail: onica1@yandex.ru
ORCID: https://orcid.org/0000-0001-8981-6599
RSCI: https://elibrary.ru/author_profile.asp?id=531209

Abstract. This paper proposes a geoinformation method for assessing the impact of hydrocarbon field development on the seismic regime of a techno-natural system by determining the quantitative relationship between hydrocarbon production parameters and seismic events recorded in the areas of developed fields. Adhering to the concept of the seismic regime of a techno-natural system in five-dimensional space, the author introduces a complex parameter of hydrocarbon field seismicity as a seismic regime parameter. This parameter represents a linear combination of normalized indicators of the sum of released energy and the density of seismic events in the spatial volume of the field under consideration over a selected period of time. To conduct the study in the oil and gas-bearing Southern Cis-Urals, where the depth of recorded seismic events does not exceed 10 km, the areal characteristics of the field territory, defined by a polygonal model, are considered instead of the spatial volume. A study matrix was constructed for which pairwise correlations were calculated between hydrocarbon production parameters and a comprehensive seismicity indicator for the field using the Orenburg oil and gas condensate field as an example. These correlations were visualized using a correlation heat map. The most important results of this study are the calculated high Pearson pairwise correlation values close to unity. These correlations enabled the identification of the key factors affecting the comprehensive natural and man-made seismicity indicator based on the obtained statistical relationship using unsupervised machine learning. A principal component regression was constructed, in which the principal components were used as predictors in the regression model instead of the initial field development parameters. The quality of the developed mathematical model for assessing the seismicity regime of the Orenburg oil and gas condensate field was determined by calculating the primary metric, the determination coefficient, which has a relatively high value of 0,88.

Keywords: geoinformation method; spatiotemporal data; man-made seismicity; mineral deposits; correlation; factor analysis; regression analysis; unsupervised machine learning of geodata

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