Hybrid Multi-Objective Optimization Approach in Water Flooding

Mohammed Al-Aghbari, Ashish M. Gujarathi*, Majid Al-Wadhahi, Nirupam Chakraborti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.
Original languageEnglish
Article number063008
Pages (from-to)1-22
Number of pages22
JournalJournal of Energy Resources Technology, Transactions of the ASME
Issue number6
Publication statusPublished - Jun 2022


  • Brugge field
  • evolutionary neural network (EvoNN)
  • multi-objective optimization
  • oil/gas reservoirs
  • petroleum engineering
  • reservoir simulation
  • waterflood optimization

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Mechanical Engineering
  • Geochemistry and Petrology

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