Permeability prediction for carbonate reservoir using a data-driven model comprising deep learning network, particle swarm optimization, and support vector regression: a case study of the LULA oilfield-Reference-Cited by-同舟云学术

Permeability prediction for carbonate reservoir using a data-driven model comprising deep learning network, particle swarm optimization, and support vector regression: a case study of the LULA oilfield

Published:2019-10 Issue:19 Volume:12 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Gu Yufeng,Bao Zhidong,Song Xinmin,Wei Mingyang,Zang Dongsheng,Niu Bo,Lu Kai

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

http://link.springer.com/content/pdf/10.1007/s12517-019-4804-3.pdf

Reference63 articles.

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3. Ahmadi MA (2012) Neural network based unified particle swarm optimization for prediction of asphaltene precipitation. Fluid Phase Equilib 314:46–51. https://doi.org/10.1016/j.fluid.2011.10.016

4. Ahmadi MA (2015a) 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

5. Ahmadi MA (2015b) Developing a robust surrogate model of chemical flooding based on the artificial neural network for enhanced oil recovery implications. Math Probl Eng 2015:706897. https://doi.org/10.1155/2015/706897

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