1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. Data Science in Oil and Gas 2021
  5. Conference paper

Abstract

Summary

Non-seismic methods (NSM) in geophysics are a crucial addition to classical seismic information. It helps to make decisions at early stages of geological exploration in case of limited information value conditions and provide a new knowledge about geological structure. While seismic exploration remains as the most spreading technique in field geophysics, non-seismic methods predominantly play a role of auxiliary methods, more often particular cases advocate self-sufficiency of NSM in application to exploration geophysical problems. The restoration of structural boundaries is especially important to restore structural boundaries in the space between seismic survey profiles. A simple solution in the form of interpolation does not provide the necessary prediction accuracy, and requires the creation of a complex, often nonlinear model, which is possible using machine learning (ML) methods. There is a large number of features at one measurement point – the values of the geophysical fields and their transformations (derivatives, filters in a window of different widths). The analysis of the importunateness of each feature before training the ML algorithm allows you to increase the accuracy of the constructed model.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202156005
2021-08-04
2024-04-24

  • From This Site

    /content/papers/10.3997/2214-4609.202156005
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. 1.Докучаева, Н. А., Арефьев, Ю. М., Андреева, Е. Е., & Ионов, Г. М. [2013] Перспективы расширения площади нефтеносности месторождений на поздней стадии их разработки на основе комплексного анализа данных геофизических методов.Георесурсы, (3 (53)), 11–13
     [Google Scholar]
  2. 2.БогословскийВ. А., ГорбачевЮ. И., ЖигалинА. Д., КалининА. В., МодинИ. Н., ПоповМ. Г., ПушкаревП. Ю., СтепановП. Ю., иХмелевской В. К. [2015] Геофизика: учебник, под ред. Хмелевского В.К., 4-е издание, КДУ Москва
    [Google Scholar]
  3. 3.BreimanL. [2001] Random forests, Machine learning. Т. 45. — №. 1. — С. 5–32.
     [Google Scholar]
  4. 4.JavaidA., IsmailM. T., AliM. K. M. [2019] Model selection for collector efficiency of seaweed drier by using LASSO and multiple regression analysis using 8sc, AIP Conference Proceedings. — AIP Publishing LLC, Т. 2184. — №. 1. — С. 050032.
    [Google Scholar]
  5. 5.HuangH. C., HsuN. J., TheobaldD., BreidtF. J. [2006] Spatial LASSO with applications to GIS model selection. — Technical Report — Т. 15.
     [Google Scholar]
  6. 6.TibshiraniR.Regression shrinkage and selection via the lasso: a retrospective [2011] Journal of the Royal Statistical Society: Series B (Statistical Methodology). — Т. 73. — №. 3. — С. 273–282.
     [Google Scholar]
  7. 7.GrigorievG., ZakharovaO., LiubimovE. et al. [2018] 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]
  8. 1.Dokuchaeva, N. A., Aref’ev, YU. M., Andreeva, E. E., & Ionov, G. M. [2013] Perspektivy rasshireniya ploshchadi neftenosnosti mestorozhdenij na pozdnej stadii ih razrabotki na osnove kompleksnogo analiza dannyh geofizicheskih metodov.Georesursy, (3 (53)), 11–13
     [Google Scholar]
  9. 2.BogoslovskijV. A., GorbachevYU. I., ZHigalinA. D., KalininA. V., ModinI. N., PopovM. G., PushkarevP. YU., StepanovP. YU., i HmelevskojV. K. [2015] Geofizika: uchebnik, pod red. Hmelevskogo V.K., 4-e izdanie, KDU Moskva
     [Google Scholar]
  10. 3.BreimanL. [2001] Random forests, Machine learning. Т. 45. — №. 1. — С. 5–32.
     [Google Scholar]
  11. 4.JavaidA., IsmailM. T., AliM. K. M. [2019] Model selection for collector efficiency of seaweed drier by using LASSO and multiple regression analysis using 8sc, AIP Conference Proceedings. — AIP Publishing LLC, Т. 2184. — №. 1. — С. 050032.
     [Google Scholar]
  12. 5.HuangH. C., HsuN. J., TheobaldD., BreidtF. J. [2006] Spatial LASSO with applications to GIS model selection. — Technical Report — Т. 15.
     [Google Scholar]
  13. 6.TibshiraniR.Regression shrinkage and selection via the lasso: a retrospective [2011] Journal of the Royal Statistical Society: Series B (Statistical Methodology). — Т. 73. — №. 3. — С. 273–282.
     [Google Scholar]
  14. 7.GrigorievG., ZakharovaO., LiubimovE. et al. [2018] 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]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202156005
Loading
Usage of Machine Learning Algorithms for Structural Boundaries Reconstruction Using The Non-Seismic Methods Data with Feature Selection
Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202156005
/content/papers/10.3997/2214-4609.202156005
/content/papers/10.3997/2214-4609.202156005
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

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