| Форма представления | Тезисы и материалы конференций в российских журналах и сборниках |
| Год публикации | 2018 |
| Язык | русский |
|
Исмагилов Амир Равилевич, автор
Нугуманова Наталья Викторовна, автор
Нургалиев Данис Карлович, автор
Судаков Владислав Анатольевич, автор
Усманов Сергей Анатольевич, автор
|
|
Муртазин Тимур Александрович, автор
|
| Библиографическое описание на языке оригинала |
A. Ismagilov. Machine Learning Approach to Open Hole Interpretation and Static Modelling Applied to a Giant Field/A. Ismagilov, V. Sudakov, D. Nurgaliev, T. Murtazin, S. Usmanov, N. Nugumanova//SPE Russian Petroleum Technology Conference.-2018.-с.1-18 |
| Аннотация |
SPE Russian Petroleum Technology Conference |
| Ключевые слова |
Machine Learning, Interpretation,Static Modelling |
| Название журнала |
SPE Russian Petroleum Technology Conference
|
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=192981 |
Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Исмагилов Амир Равилевич |
ru_RU |
| dc.contributor.author |
Нугуманова Наталья Викторовна |
ru_RU |
| dc.contributor.author |
Нургалиев Данис Карлович |
ru_RU |
| dc.contributor.author |
Судаков Владислав Анатольевич |
ru_RU |
| dc.contributor.author |
Усманов Сергей Анатольевич |
ru_RU |
| dc.contributor.author |
Муртазин Тимур Александрович |
ru_RU |
| dc.date.accessioned |
2018-01-01T00:00:00Z |
ru_RU |
| dc.date.available |
2018-01-01T00:00:00Z |
ru_RU |
| dc.date.issued |
2018 |
ru_RU |
| dc.identifier.citation |
A. Ismagilov. Machine Learning Approach to Open Hole Interpretation and Static Modelling Applied to a Giant Field/A. Ismagilov, V. Sudakov, D. Nurgaliev, T. Murtazin, S. Usmanov, N. Nugumanova//SPE Russian Petroleum Technology Conference.-2018.-с.1-18 |
ru_RU |
| dc.identifier.uri |
https://repository.kpfu.ru/?p_id=192981 |
ru_RU |
| dc.description.abstract |
SPE Russian Petroleum Technology Conference |
ru_RU |
| dc.description.abstract |
This paper introduces description and results of creating a complex method for automatic interpretation of well-logging data and the further construction of the first approximation of the geological model. That procedure is aimed at mass re-interpretation of a large number of wells in terrigenous deposits of Tula and Bobrikovian horizons of Tatarstan. It was solved with the use of machine learning methods and artificial neural networks. We also proposed an improved solution to the problem of determining the reference value for normalization of neutron logging in the absence of reference horizon data. The priority task of well logs depth matching is solved by use of correlation coefficient, logistic regression and the idea that the expert has a different preference to different depth intervals in the investigated horizon. The next issue is the stratigraphic division which also was solved by logistic regression training as the most |
ru_RU |
| dc.language.iso |
ru |
ru_RU |
| dc.subject |
Machine Learning |
ru_RU |
| dc.subject |
Interpretation |
ru_RU |
| dc.subject |
Static Modelling |
ru_RU |
| dc.title |
Machine Learning Approach to Open Hole Interpretation and Static Modelling Applied to a Giant Field |
ru_RU |
| dc.type |
Тезисы и материалы конференций в российских журналах и сборниках |
ru_RU |
|
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