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

2 Citations (Scopus)

Abstract

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 (EvoNN) algorithm 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 and 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
JournalJournal of Energy Resources Technology, Transactions of the ASME
Volume144
Issue number6
DOIs
Publication statusPublished - Jun 2022

Keywords

  • Brugge field
  • evolutionary neural network (EvoNN)
  • multi-objective optimization
  • NSGA-II
  • 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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