Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability-Reference-Cited by-同舟云学术

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Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability

Published:2022-12-20 Issue:13 Volume:30 Page:35872-35890
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Gao Cen,Lu Pu-Huai,Ye Wei-Min,Liu Zhang-Rong,Wang Qiong,Chen Yong-Gui

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-022-24558-5.pdf

Reference88 articles.

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2. Ahmadi MA (2015) Connectionist approach estimates gas-oil relative permeability in petroleum reservoirs: application to reservoir simulation. Fuel 140:429–439. https://doi.org/10.1016/j.fuel.2014.09.058

3. Alavi AH, Gandomi AH, Sahab MG, Gandomi M (2010) Multi expression programming: a new approach to formulation of soil classification. Engineering with Computers 26(2):111–118. https://doi.org/10.1007/s00366-009-0140-7

4. Amann-Hildenbrand A, Bertier P, Busch A, Krooss BM (2013) Experimental investigation of the sealing capacity of generic clay-rich caprocks. Int J Greenh Gas Control 19:620–641. https://doi.org/10.1016/j.ijggc.2013.01.040

5. Ao YL, Li HQ, Zhu LP, Ali S, Yang ZG (2019) The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Pet Sci Eng 174:776–789. https://doi.org/10.1016/j.petrol.2018.11.067

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