KFU. Card publication. Machine Learning Approach to Open Hole Interpretation and Static Modelling Applied to a Giant Field

KAZAN
FEDERAL UNIVERSITY

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MACHINE LEARNING APPROACH TO OPEN HOLE INTERPRETATION AND STATIC MODELLING APPLIED TO A GIANT FIELD

Form of presentation

Conference proceedings in Russian journals and collections

Year of publication

2018

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Authors, employees of KFU

Ismagilov Amir Ravilevich, author

Nugumanova Natalya Viktorovna, author

Nurgaliev Danis Karlovich, author

Sudakov Vladislav Anatolevich, author

Usmanov Sergey Anatolevich, author

Authors, postgraduates KFU

Murtazin Timur Aleksandrovich, postgraduate kfu

Bibliographic description in the original language

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.-s.1-18

Annotation

SPE Russian Petroleum Technology Conference

Keywords

Machine Learning, Interpretation,Static Modelling

The name of the journal

SPE Russian Petroleum Technology Conference

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=192981&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Ismagilov Amir Ravilevich	ru_RU
dc.contributor.author	Nugumanova Natalya Viktorovna	ru_RU
dc.contributor.author	Nurgaliev Danis Karlovich	ru_RU
dc.contributor.author	Sudakov Vladislav Anatolevich	ru_RU
dc.contributor.author	Usmanov Sergey Anatolevich	ru_RU
dc.contributor.author	Murtazin Timur Aleksandrovich	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.-�.1-18	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=192981&p_lang=2	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	Conference proceedings in Russian journals and collections	ru_RU