Open Access
E3S Web Conf.
Volume 266, 2021
Topical Issues of Rational Use of Natural Resources 2021
Article Number 07005
Number of page(s) 7
Section Geological Mapping, Exploration, and Prospecting of Mineral Resources
Published online 04 June 2021
  1. Yuriev, A.V., Gurbatova P., Melekhin S. V. Geology, Geophysics and development of oil and gas structures Features of studying petro physical and elastic properties of the core in complex reservoirs of oil and gas when modeling thermo baric formation conditions 5, 67–72 (2010). [Google Scholar]
  2. Du K.L., Swamy M.N.S. Springer Fundamentals of Machine Learning. In: Neural Networks and Statistical Learning (2014). [Google Scholar]
  3. Hamada, G., Elsakka A. Petroleum engineering department, Faculty of geosciences and petroleum engineering, University technology Petronas Artificial neural network (ann) prediction of porosity and water saturation of Shaly sandstone reservoirs, Malaysia (2018) [Google Scholar]
  4. Hertz, J. Krogh, A., Palmer, R. Press Westview Introduction to the theory of neural computation (1991). [Google Scholar]
  5. Murtazin T., Ismagilov A., Novikiva S., Sudakov V. SGEM Methods of automation the process of detailed correlation of well sections with the use of machine learning (2019) [Google Scholar]
  6. Platov, B., Kozhevnikova, N., Shipaeva, M. SGEM The example of neural net algorithm applying for seismic facies analysis. Example from the republic of Tatarstan (2019) [Google Scholar]
  7. Platov, B., Safina, R., Zinjukov, R. SGEM Seismic facies analysis of the carboniferous reservoir. Case study from the Tatarstan (2018) [Google Scholar]
  8. Podolsky, A.K. Modern scienceApplication of artificial intelligence methods in the oil and gas industry 3, 33–36 (2016). [Google Scholar]
  9. Stepanov A., Murtazin T., Ismagilov A., Delev A., SGEM Use of an artificial neural network algorithm and cokriging method for reservoir porosity modeling (2019) [Google Scholar]
  10. Validov, M., Ismagilov, A.R., Voloskov, D., Magdeyev, M.S., Nazarov, A.A. EAGE Geomodel Development of the Approach for Automatic Well Logging Interpretation for Big Number of Wells with the Use of Machine Learning (2017) [Google Scholar]
  11. Yang, Y., Aplin A.C., Larter S.R. Petroleum geoscienceQuantitative assessment of mud-stone lithology using geophysical wireline logs and artificial neural networks 10, 141–151 (2004) [Google Scholar]

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