On the prediction of pseudo relative permeability curves: meta-heuristics versus Quasi-Monte Carlo
Institute of Enhanced Oil Recovery, Center for Exploration and Production Studies and Research, Research Institute of Petroleum Industry (RIPI), PO Box 14665-1998, Tehran, Iran
* Corresponding author: email@example.com
Accepted: 6 March 2019
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.
© B. Fazelabdolabadi et al., published by IFP Energies nouvelles, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.