On the prediction of pseudo relative permeability curves: meta-heuristics versus Quasi-Monte Carlo
This article reports the first application of the Quasi-Monte Carlo (QMC) method for estimation of the pseudo relative permeability curves. In this regards, the performance of several meta-heuristics algorithms have also been compared versus QMC, including the Genetic Algorithm (GA), Particle Swarm...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2019-01-01
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| Series: | Oil & Gas Science and Technology |
| Online Access: | https://ogst.ifpenergiesnouvelles.fr/articles/ogst/full_html/2019/01/ogst180086/ogst180086.html |
| Summary: | This article reports the first application of the Quasi-Monte Carlo (QMC) method for estimation of the pseudo relative permeability curves. In this regards, the performance of several meta-heuristics algorithms have also been compared versus QMC, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Artificial Bee Colony (ABC). The mechanism of minimizing the objective-function has been studied, for each method. The QMC has outperformed its counterparts in terms of accuracy and efficiently sweeping the entire search domain. Nevertheless, its computational time requirement is obtained in excess to the meta-heuristics algorithms. |
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| ISSN: | 1294-4475 1953-8189 |