A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization

This review illustrates the most recent improvements and implementations of ANN in characterizing reservoirs in different regions for faster understanding of young petroleum geoscientists and engineers in the industry. Artificial Neural Networks (ANNs), one of the AI tools, has been effectively empl...

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Main Authors: Yazmyradova, G., Hermana, M., Soleimani, H., Sivabalan, V.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.33162 /
Published: Slovnaft VURUP a.s 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132970075&partnerID=40&md5=bb2423c2229f6cdf975af422893b839e
http://eprints.utp.edu.my/33162/
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id utp-eprints.33162
recordtype eprints
spelling utp-eprints.331622022-07-06T08:05:03Z A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization Yazmyradova, G. Hermana, M. Soleimani, H. Sivabalan, V. This review illustrates the most recent improvements and implementations of ANN in characterizing reservoirs in different regions for faster understanding of young petroleum geoscientists and engineers in the industry. Artificial Neural Networks (ANNs), one of the AI tools, has been effectively employed in several domains and has also gained popularity in reservoir characterization. Reservoir characterization is the act of creating a reservoir model based on its characteristics that are important to its capability to produce and store hydrocarbons with respect to fluid flow. The reservoir characterization domain is tricky because of the high complexity of non-linear data and ambiguity in data and modelling. The major objective of ANN application is to integrate obtained data from various geological, geophysical, petrophysical sources in reservoir characterization by identifying the complex non-linear correlation of input data. This work serves as an insight of the current implementation of ANN in the industry, which encourages more innovative intelligence systems that could accelerate the improvements of reservoir characterization evaluation protocols. © 2022. Petroleum and Coal.All Rights Reserved. Slovnaft VURUP a.s 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132970075&partnerID=40&md5=bb2423c2229f6cdf975af422893b839e Yazmyradova, G. and Hermana, M. and Soleimani, H. and Sivabalan, V. (2022) A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization. Petroleum and Coal, 64 (2). pp. 228-242. http://eprints.utp.edu.my/33162/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This review illustrates the most recent improvements and implementations of ANN in characterizing reservoirs in different regions for faster understanding of young petroleum geoscientists and engineers in the industry. Artificial Neural Networks (ANNs), one of the AI tools, has been effectively employed in several domains and has also gained popularity in reservoir characterization. Reservoir characterization is the act of creating a reservoir model based on its characteristics that are important to its capability to produce and store hydrocarbons with respect to fluid flow. The reservoir characterization domain is tricky because of the high complexity of non-linear data and ambiguity in data and modelling. The major objective of ANN application is to integrate obtained data from various geological, geophysical, petrophysical sources in reservoir characterization by identifying the complex non-linear correlation of input data. This work serves as an insight of the current implementation of ANN in the industry, which encourages more innovative intelligence systems that could accelerate the improvements of reservoir characterization evaluation protocols. © 2022. Petroleum and Coal.All Rights Reserved.
format Article
author Yazmyradova, G.
Hermana, M.
Soleimani, H.
Sivabalan, V.
spellingShingle Yazmyradova, G.
Hermana, M.
Soleimani, H.
Sivabalan, V.
A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization
author_sort Yazmyradova, G.
title A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization
title_short A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization
title_full A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization
title_fullStr A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization
title_full_unstemmed A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization
title_sort bird�s eye view on the applications of neural networks in reservoir characterization
publisher Slovnaft VURUP a.s
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132970075&partnerID=40&md5=bb2423c2229f6cdf975af422893b839e
http://eprints.utp.edu.my/33162/
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score 11.62408

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