Skip Nav Destination
Close Modal
Update search
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
NARROW
Format
Subjects
Article Type
Date
Availability
1-2 of 2
Keywords: solution gas-oil ratio
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Proceedings Papers
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 29–31, 2019
Paper Number: SPE-196453-MS
... in modeling. The proposed model predicts the bubble point pressure ( P b ) and the oil formation volume factor at bubble point pressure ( B ob ) as a function of oil and gas specific gravity, solution gas-oil ratio, and reservoir temperature by a boosted decision tree regression (BDTR) predictive modeling...
Proceedings Papers
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, October 12–14, 1998
Paper Number: SPE-49961-MS
... Abstract This paper presents a new technique to model the behavior of crude oil and natural gas systems. The proposed technique is using a radial basis function neural network model (RBFNM). The model predicts oil formation volume factor, solution gas- oil ratio, oil viscosity, saturated oil...