Prediction of The Structural Basement Surface Using Machine Learning Algorithms Applied in Potential Geophysical Fields

References

1. CracknellM. J., ReadingA. M.
   Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information //Computers & Geosciences. 2014. V. 63. p. 128.
   [Google Scholar]
2. Fernandez-PopovaA., GulinV., SenchinaN., MingalevaT.
   Dependence evaluation of machine prediction quality the structural surfaces by potential fields on the completeness of the training sample. The first regional EAGE conference in Russia and the CIS countries “Data mining in the oil and gas industry”, 2020.
   [Google Scholar]
3. GrigorievG., ZakharovaO., LiubimovE. et al.
   Non-Seismic Methods Development at Gazprom Neft. SPE Russian Petroleum Technology Conference, 15–17 October. DOI: 10.2118/191670‑18RPTC‑MS.
   https://doi.org/10.2118/191670-18RPTC-MS. [Google Scholar]
4. GrigorievG., SenchinaN.
   Model assessments of the relevance of gravimetric monitoring in the development of oil rims. Geobaikal 2020.
   [Google Scholar]
5. GulinV., GrigorievG., SalishchevM.
   Improving the efficiency of area exploration projects through the preliminary modeling stage. ProGREss 2019: Exploration as a Business - Oil and Gas International Exploration Conference, 2019.
   [Google Scholar]
6. Katuntsov, E.V., Kultan, J., Makhovikov, A.B.
   Application of electronic learning tools for training of specialists in the field of information technologies for enterprises of mineral resources sector. Journal of Mining Institute, 2017, 226, p. 503–508
   [Google Scholar]
7. MakhovikovA. B., KatuntsovE. V., KosarevO. V., TsvetkovP. S.
   Digital Transformation In Oil And Gas Extraction / Innovation-Based Development of the Mineral Resources Sector: Challenges And Prospects, № 11, Т 1, 2018. p. 531–538.
   [Google Scholar]
8. MovchanI.B., YakovlevaA.A.
   Wave analogies in the quantitative interpretation of potential fields. International Journal of Advanced Trends in Computer Science and Engineering, 2020, 9(2), p. 1793–1799. DOI: 10.30534/ijatcse/2020/136922020.
   https://doi.org/10.30534/ijatcse/2020/136922020. [Google Scholar]
9. Putikov, O., Kholmyanski, M., Ivanov, G., Senchina, N.
   Application of geoelectrochemical method for exploration of petroleum fields on the Arctic shelf. Chemie der Erde, 2020, 80(3), 125498. DOI: 10.1016/j.geoch.2019.02.001.
   https://doi.org/10.1016/j.geoch.2019.02.001. [Google Scholar]
10. Sysoev, A.P., Gorelik, G.D.
    Parametric method of compensation for near-surface heterogeneity in processing CDP data. Russian Geology and Geophysics, 2017, 58(6), p. 763–768.
    [Google Scholar]
11. SolomonA., ChuanboS., YaoY., MercyM., XiangfengZ.
    Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability. Energies2018, 11, 3261.
    [Google Scholar]
12. Rodriguez-GalianoV., Sanchez-CastilloM., Chica-OlmoM., Chica-RivasM. J. O. G. R.
    Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines // Ore Geology Reviews. - 2015. - V. 71. - p. 804–818.
    [Google Scholar]