TY - JOUR
T1 - Hybrid Multi-Objective Optimization Approach in Water Flooding
AU - Al-Aghbari, Mohammed
AU - Gujarathi, Ashish M.
AU - Al-Wadhahi, Majid
AU - Chakraborti, Nirupam
N1 - Publisher Copyright:
Copyright © 2021 by ASME.
PY - 2022/6
Y1 - 2022/6
N2 - Non-dominated Sorting Genetic Algorithm, second version (NSGA-II) is used as a stochastic optimization technique successfully in different engineering applications. In this study, a data-driven optimization strategy based upon evolutionary neural network algorithm (EvoNN) is developed for providing input into NSGA-II optimization. Evolutionary neural-network data-driven model is built and trained using initial solutions generated by NSGA-II optimization coupled with the reservoir simulation model. Evolutionary optimization incorporated in the EvoNN strategy is applied in the trained data-driven model to generate the Pareto optimal solution, which is then used as a guiding input into NSGA-II optimization. The described method is applied in two case studies (i.e. Brugge field model & water injection pattern model). The Pareto optimal solutions obtained with data-driven model guided NSGA-II in both models show improvement in convergence and diversity of the solution. The convergence to the Pareto optimal solution has improved by 9% for case-1 (i.e. Brugge field) and by 43% for case-2 (i.e. water injection pattern model). In addition, the Pareto optimal solution obtained by the proposed hybridization has shown improvement in the water oil ratio (WOR) up to 6% in the Brugge field and up to 97% in the water injection pattern model. This improvement can lead to wide applications in using evolutionary optimizations in real field simulation models at acceptable computation time.
AB - Non-dominated Sorting Genetic Algorithm, second version (NSGA-II) is used as a stochastic optimization technique successfully in different engineering applications. In this study, a data-driven optimization strategy based upon evolutionary neural network algorithm (EvoNN) is developed for providing input into NSGA-II optimization. Evolutionary neural-network data-driven model is built and trained using initial solutions generated by NSGA-II optimization coupled with the reservoir simulation model. Evolutionary optimization incorporated in the EvoNN strategy is applied in the trained data-driven model to generate the Pareto optimal solution, which is then used as a guiding input into NSGA-II optimization. The described method is applied in two case studies (i.e. Brugge field model & water injection pattern model). The Pareto optimal solutions obtained with data-driven model guided NSGA-II in both models show improvement in convergence and diversity of the solution. The convergence to the Pareto optimal solution has improved by 9% for case-1 (i.e. Brugge field) and by 43% for case-2 (i.e. water injection pattern model). In addition, the Pareto optimal solution obtained by the proposed hybridization has shown improvement in the water oil ratio (WOR) up to 6% in the Brugge field and up to 97% in the water injection pattern model. This improvement can lead to wide applications in using evolutionary optimizations in real field simulation models at acceptable computation time.
KW - Brugge field
KW - NSGA-II
KW - evolutionary neural network (EvoNN)
KW - multi-objective optimization
KW - oil/gas reservoirs
KW - petroleum engineering
KW - reservoir simulation
KW - waterflood optimization
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UR - https://www.mendeley.com/catalogue/b6a2c2a3-2ec4-39f5-9c5e-05fbbefbf65b/
U2 - 10.1115/1.4052623
DO - 10.1115/1.4052623
M3 - Article
AN - SCOPUS:85134233243
SN - 0195-0738
VL - 144
SP - 1
EP - 22
JO - Journal of Energy Resources Technology, Transactions of the ASME
JF - Journal of Energy Resources Technology, Transactions of the ASME
IS - 6
M1 - 063008
ER -