KFU. Card publication. STATISTICAL AND NEURAL NETWORK METHODS FOR LOCALIZING RESIDUAL OIL RESERVES

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STATISTICAL AND NEURAL NETWORK METHODS FOR LOCALIZING RESIDUAL OIL RESERVES

Form of presentation

Conference proceedings in Russian journals and collections

Year of publication

2021

Язык

русский

Authors, employees of KFU

Zinyukov Rustam Anverovich, author

Petrov Timur Sergeevich, author

Poryvaev Timur Maratovich, author

Safuanov Rinat Iolduzovich, author

Other authors

Fakhrutdinov Ildar Zufarovich, author

Bibliographic description in the original language

Safuanov, Rinat I., Rustam Anverovich Ziniukov, Timur Poryvaev, Timur Petrov and Ildar Farkhutdiniov. “STATISTICAL AND NEURAL NETWORK METHODS FOR LOCALIZING RESIDUAL OIL RESERVES.” SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings (2021): 993-1000

Annotation

International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEMЭта ссылка отключена., 2021,

Keywords

Residual oil reserves, neural networks

The name of the journal

International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEMЭта ссылка отключена., 2021,

On-line resource for training course

http://dspace.kpfu.ru/xmlui/bitstream/handle/net/183752/STATISTICAL_AND_NEURAL_NETWORK_MET_ODS_FOR_LOCALIZING.pdf?sequence=1&isAllowed=y

URL

https://www.sgem.org/index.php/elibrary-research-areas?view=publication&task=show&id=7847

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

https://repository.kpfu.ru/eng/?p_id=300168&p_lang=2

Resource files

File name	Size (MB)	Format
STATISTICAL_AND_NEURAL_NETWORK_MET_ODS_FOR_LOCALIZING.pdf	2,56	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Zinyukov Rustam Anverovich	ru_RU
dc.contributor.author	Petrov Timur Sergeevich	ru_RU
dc.contributor.author	Poryvaev Timur Maratovich	ru_RU
dc.contributor.author	Safuanov Rinat Iolduzovich	ru_RU
dc.contributor.author	Fakhrutdinov Ildar Zufarovich	ru_RU
dc.date.accessioned	2021-01-01T00:00:00Z	ru_RU
dc.date.available	2021-01-01T00:00:00Z	ru_RU
dc.date.issued	2021	ru_RU
dc.identifier.citation	Safuanov, Rinat I., Rustam Anverovich Ziniukov, Timur Poryvaev, Timur Petrov and Ildar Farkhutdiniov. “STATISTICAL AND NEURAL NETWORK METHODS FOR LOCALIZING RESIDUAL OIL RESERVES.” SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings (2021): 993-1000	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=300168&p_lang=2	ru_RU
dc.description.abstract	International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEMЭта ссылка отключена., 2021,	ru_RU
dc.description.abstract	The determination of residual oil reserves is a complex task that involves high qualified specialists and resource-intensive calculations. Classical oil reservoir simulation methods cannot provide proper level of reliability due to increasing of complexity caused by oil reservoir depleting. Stagnant oil zones arising from heterogeneity of shear modulus of medium cannot be determinate by oil reservoir simulation due to use of effective values of porosity and absolute and phase permeability. The problem of determination of stagnant zones can be solved by using more flexible methods as a machine learning in general and the neural network in particular. The flexibility of these methods allow to build a model that can consider both physical properties of reservoir and the it operating history. In this paper the model based on Convolutional Neural Network (CNN) was built. The choice of CNN based on the assumption that this type of neural network does analysis in a similar way to a human. CNN is also simplifying the approach to distance depended values because the actual coordinate dependence which may not be the continuous function due to border conditions replaces with zone-based dependence produced by convolution. In this case the predicting value function becomes continuous and satisfies Cybenko theorem. The main idea of this model is transforming neighbor wells data of some point in 2D space into nxn matrix which is similar to grayscale image. The stack of matrices passes to the CNN as different channels of full-color image to find dependencies between image and an actual value which will be predict after successful learning.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Residual oil reserves	ru_RU
dc.subject	neural networks	ru_RU
dc.title	STATISTICAL AND NEURAL NETWORK METHODS FOR LOCALIZING RESIDUAL OIL RESERVES	ru_RU
dc.type	Conference proceedings in Russian journals and collections	ru_RU