Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models-Reference-Cited by-同舟云学术

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Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models

Published:2023-11-18 Issue:4 Volume:36 Page:1973-1995
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Esmaeili-Jaghdan Zohre,Tatar Afshin,Shokrollahi Amin^ORCID,Bon Jan,Zeinijahromi Abbas

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-09201-9.pdf

Reference98 articles.

1. Bozorgzadeh M, Gringarten AC (2006) condensate bank characterization from well test data and fluid PVT properties. SPE Reserv Evaluat Eng 9(05):596–611

2. Fevang O (1995) Gas condensate flow behavior and sampling. Division of Petroleum Engineering and Applied Geophysics

3. Fasesan S, Olukini O, Adewumi O (2003) Characteristics of gas condensate. Petroleum Sci Technol 21(1–2):81–90

4. McCain WD Jr (1973) Properties of petroleum fluids. Petroleum Publishing Co., Tulsa, OK

5. El-Banbi AH, McCain WD, Jr., Semmelbeck ME (2000) Investigation of well productivity in gas-condensate reservoirs. SPE/CERI gas technology symposium

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