Comparing Two Methods of Neural Networks to Evaluate Dead Oil Viscosity Article Swipe
Meysam Dabiri-Atashbeyk
,
Mehdi Koolivand-salooki
,
Morteza Esfandyari
,
Mohsen Koulivand
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.22050/ijogst.2017.70576.1373
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.22050/ijogst.2017.70576.1373
Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm) were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doaj.org/article/0132e5396948475795594bdecb7c6a24
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2788542512
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2788542512Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22050/ijogst.2017.70576.1373Digital Object Identifier
- Title
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Comparing Two Methods of Neural Networks to Evaluate Dead Oil ViscosityWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
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Meysam Dabiri-Atashbeyk, Mehdi Koolivand-salooki, Morteza Esfandyari, Mohsen KoulivandList of authors in order
- Landing page
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https://doaj.org/article/0132e5396948475795594bdecb7c6a24Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://doaj.org/article/0132e5396948475795594bdecb7c6a24Direct OA link when available
- Concepts
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Viscosity, Artificial neural network, Petroleum engineering, Environmental science, Computer science, Artificial intelligence, Thermodynamics, Geology, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2020: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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