Multi-objective global and local Surrogate-Assisted optimization on polymer flooding
•An novel stochastic method is proposed to conduct multi-objective optimization.•Proxy-based optimization is involved to improve computational efficiency.•Two applications on polymer flooding cases are discussed and compared with other multi-objective methods. Oil production and polymer injection ar...
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| Veröffentlicht in: | Fuel (Guildford) Jg. 342; S. 127678 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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15.06.2023
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| ISSN: | 0016-2361, 1873-7153 |
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| Abstract | •An novel stochastic method is proposed to conduct multi-objective optimization.•Proxy-based optimization is involved to improve computational efficiency.•Two applications on polymer flooding cases are discussed and compared with other multi-objective methods.
Oil production and polymer injection are two performance indicators of polymer flooding and are usually conflicting objectives. In order to obtain optimal trade-off solutions, this paper proposes a multi-objective global and local surrogate-assisted particle swarm optimization (MO-GLSPSO) method, which consists of alternative steps: global population prescreen and local population search.
The global steps use generalized regression neural network (GRNN) to prescreen a better population, and the local steps use radial basis function (RBF) as proxy to search for the next generation. The global steps aim to reduce the chance of generations being trapped in local minima, and the local steps obtain the optimal solutions with a fast convergence rate. The rates (liquid production rate and water injection rate) and polymer injection concentration of wells are tuned to obtain a Pareto-front that maximizes cumulative oil production and minimizes cumulative polymer injection.
The MO-GLSPSO method is tested using both synthetic and Brugge benchmark cases. The iterations generally improve the oil production or reduce polymer injection and are stabilized at a Pareto-front of the two objectives. Improved sweep efficiency and polymer utility are also observed in the optimal results. The proposed method is also compared with other two methods, multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), to examine the pros and cons. The results indicate that MO-GLSPSO has a better pareto-front than others. |
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| AbstractList | •An novel stochastic method is proposed to conduct multi-objective optimization.•Proxy-based optimization is involved to improve computational efficiency.•Two applications on polymer flooding cases are discussed and compared with other multi-objective methods.
Oil production and polymer injection are two performance indicators of polymer flooding and are usually conflicting objectives. In order to obtain optimal trade-off solutions, this paper proposes a multi-objective global and local surrogate-assisted particle swarm optimization (MO-GLSPSO) method, which consists of alternative steps: global population prescreen and local population search.
The global steps use generalized regression neural network (GRNN) to prescreen a better population, and the local steps use radial basis function (RBF) as proxy to search for the next generation. The global steps aim to reduce the chance of generations being trapped in local minima, and the local steps obtain the optimal solutions with a fast convergence rate. The rates (liquid production rate and water injection rate) and polymer injection concentration of wells are tuned to obtain a Pareto-front that maximizes cumulative oil production and minimizes cumulative polymer injection.
The MO-GLSPSO method is tested using both synthetic and Brugge benchmark cases. The iterations generally improve the oil production or reduce polymer injection and are stabilized at a Pareto-front of the two objectives. Improved sweep efficiency and polymer utility are also observed in the optimal results. The proposed method is also compared with other two methods, multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), to examine the pros and cons. The results indicate that MO-GLSPSO has a better pareto-front than others. |
| ArticleNumber | 127678 |
| Author | Zhang, Ruxin Chen, Hongquan |
| Author_xml | – sequence: 1 givenname: Ruxin surname: Zhang fullname: Zhang, Ruxin email: ruxinzhang@tamu.edu – sequence: 2 givenname: Hongquan surname: Chen fullname: Chen, Hongquan email: chenhongquan@tamu.edu |
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| Cites_doi | 10.2118/209608-PA 10.2118/187298-MS 10.1109/72.97934 10.1016/j.petrol.2021.109116 10.1109/4235.996017 10.2118/200388-MS 10.1109/ICNN.1995.488968 10.2118/143067-MS 10.1002/ese3.1276 10.1007/978-3-642-15844-5_37 10.1016/j.petrol.2017.03.026 10.1016/j.petrol.2014.11.006 10.1109/4235.797969 10.1007/s00158-014-1125-8 10.1109/TCYB.2018.2809430 10.2523/IPTC-19314-MS 10.1109/TNN.2002.1000134 10.1016/j.petrol.2013.11.006 10.1016/j.petrol.2018.03.062 10.1109/TEVC.2017.2675628 10.1016/j.fuel.2021.122600 10.2118/124815-MS 10.1016/j.jngse.2019.103038 10.2118/182598-PA |
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| Keywords | Radial basis function Generalized regression neural network Multi-objective optimization method Particle swarm optimization Uncertainty analysis |
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| SubjectTerms | Generalized regression neural network Multi-objective optimization method Particle swarm optimization Radial basis function Uncertainty analysis |
| Title | Multi-objective global and local Surrogate-Assisted optimization on polymer flooding |
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