1887

Abstract

Summary

This paper investigate the possibility and effectiveness of using different machine learning algorithms to predict the spatial distribution of physical properties of rocks based on the joint analysis of 3D seismic data and well logs.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201800115
2018-04-09
2024-03-29
Loading full text...

Full text loading...

References

  1. КлейтонВ.
    , Дойч Геостатистическое моделирование коллекторов М. Ижевск: Институт компьютерных исследований, 2011, 400 с.
    [Google Scholar]
  2. ТихоновА.Н., АрсенинВ.Я.
    Методы решения некорректных задач. М.: Наука. Главная редакция физико-математической литературы, 1979. Изд. 2-е. 288 с.
    [Google Scholar]
  3. ЛевянтВ.Б., ХромоваИ.Ю., КозловЕ.А. идр.
    Методические рекомендации по использованию данных сейсморазведки для подсчета запасов углеводородов в условиях карбонатных пород с пористостью трещинно-кавернового типа. ЦГЭ, Москва, 2010, 250 с.
    [Google Scholar]
  4. ФлахП.
    Машинное обучение. Наука и искусство построения алгоритмов, которые извлекают знания из данных, ДМК пресс, 2015, 400 с.
    [Google Scholar]
  5. ЯковлевИ.В., АмпиловЮ.П., ФилипповаК.Е.
    Почти всё о сейсмической инверсии Часть 2. Технологии сейсморазведки, № 1, 2011, с. 5–15.
    [Google Scholar]
  6. Barnett, R. M., and C. V.Deutsch
    , 2013, Tutorial and tools for ACE regression and transformation: Centre for Computational Geostatistics (CCG) Annual Report, 15, 401: University of Alberta
    [Google Scholar]
  7. Breiman, L. and Friedman, J. H.
    (1985). “Estimating Optimal Transformations for Multiple Regression and Correlation.” (with discussion) J. Amer. Statist. Assoc.80, 580.
    [Google Scholar]
  8. Box, G.E.P. and Cox, D.R.
    (1964), An analysis of transformations, Journal of the Royal Statistical Society, Series B, Methodological, 26, 211–252.
    [Google Scholar]
  9. EmmanueT. Schnetzler, DavidL.
    Alumbaugh The use of predictive analytics for hydrocarbon exploration in the Denver-Julesburg Basin THE LEADING EDGE, 2017, 227–233.
    [Google Scholar]
  10. L.Breiman
    Random Forests Statistics Department University of California Berkeley, 2001, 33.
    [Google Scholar]
  11. KobrunovA., I.Priezzhev
    , Hybrid combination genetic algorithm and controlled gradient method to train a neural network, GEOPHYSICS, 2016, VOL. 81, NO. 4, 1–9.
    [Google Scholar]
  12. Wang, D., and M.Murphy
    , 2004, Estimating optimal transformations for multiple regression using the ACE algorithm: Journal of Data Science: 2, 329–346.
    [Google Scholar]
  13. KlejtonV.
    , Dojch Geostatisticheskoe modelirovanie kollektorov M. Izhevsk: Institut komp’yuternyh issledovanij, 2011, 400 s.
    [Google Scholar]
  14. TihonovA.N., ArseninV.YA.
    Metody resheniya nekorrektnyh zadach. M.: Nauka. Glavnaya redakciya fiziko-matematicheskoj literatury, 1979. Izd. 2-e. 288 s.
    [Google Scholar]
  15. LevyantV.B., HromovaI.YU., KozlovE.A. idr.
    Metodicheskie rekomendacii po ispol’zovaniyu dannyh sejsmorazvedki dlya podscheta zapasov uglevodorodov v usloviyah karbonatnyh porod s poristost’yu treshchinno-kavernovogo tipa. CGEH, Moskva, 2010, 250 s.
    [Google Scholar]
  16. FlahP.
    Mashinnoe obuchenie. Nauka i iskusstvo postroeniya algoritmov, kotorye izvlekayut znaniya iz dannyh, DMK press, 2015, 400 s.
    [Google Scholar]
  17. YAkovlevI.V., AmpilovYU.P., FilippovaK.E.
    Pochti vsyo o sejsmicheskoj inversii CHast’ 2. Tekhnologii sejsmorazvedki, № 1, 2011, s. 5–15.
    [Google Scholar]
  18. Barnett, R. M., and C. V.Deutsch
    , 2013, Tutorial and tools for ACE regression and transformation: Centre for Computational Geostatistics (CCG) Annual Report, 15, 401: University of Alberta
    [Google Scholar]
  19. Breiman, L. and Friedman, J. H.
    (1985). “Estimating Optimal Transformations for Multiple Regression and Correlation.” (with discussion) J. Amer. Statist. Assoc.80, 580.
    [Google Scholar]
  20. Box, G.E.P. and Cox, D.R.
    (1964), An analysis of transformations, Journal of the Royal Statistical Society, Series B, Methodological, 26, 211–252.
    [Google Scholar]
  21. EmmanueT. Schnetzler, DavidL.
    Alumbaugh The use of predictive analytics for hydrocarbon exploration in the Denver-Julesburg Basin THE LEADING EDGE, 2017, 227–233.
    [Google Scholar]
  22. L.Breiman
    Random Forests Statistics Department University of California Berkeley, 2001, 33.
    [Google Scholar]
  23. KobrunovA., I.Priezzhev
    , Hybrid combination genetic algorithm and controlled gradient method to train a neural network, GEOPHYSICS, 2016, VOL. 81, NO. 4, 1–9.
    [Google Scholar]
  24. Wang, D., and M.Murphy
    , 2004, Estimating optimal transformations for multiple regression using the ACE algorithm: Journal of Data Science: 2, 329–346.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201800115
Loading
/content/papers/10.3997/2214-4609.201800115
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error