Multiobjective design and optimization of polymer flood performance Academic Article uri icon

abstract

  • © 2017 Elsevier B.V. The design and optimization of polymer flood projects have traditionally focused on increasing oil production only. For economic viability, the optimization strategy should maximize oil production while minimizing polymer usage. However, these two objectives can be conflicting. The challenge is how to optimize polymer flood design considering the trade-off between these conflicting objectives. In this work, we utilize the concept of Pareto optimality to generate a set of Pareto optimal solutions that represent a trade-off relation between the conflicting objectives viz. polymer utility factor and incremental oil gain. The Pareto optimal solutions can be searched by a non-domination based genetic algorithm (NSGA-II). The algorithm evaluates fitness by a dominance relationship instead of fitness measures as in the ordinary single-objective genetic algorithm. The dominance concept defines levels of optimality by assigning several ranks to each population. The algorithm iteratively minimizes ranks until all members of the population become rank one which implies that the Pareto optimality condition is reached. The application of our proposed method is demonstrated by a 2D synthetic example and a 3D field-scale application. The Pareto-based approach is implemented to simultaneously optimize two objectives: (1) polymer utility factor (UF) which is the amount of polymer required per barrel of incremental oil and (2) cumulative incremental oil production (Np). The results demonstrate that beyond the optimal design, relatively minor increase in oil production requires significantly more polymer injection and the polymer UF is increased substantially. To optimize both oil production and polymer utility, the Pareto optimal solutions provide the best compromise that enables maximizing oil production while maintaining the efficiency of polymer usage.

author list (cited authors)

  • Ekkawong, P., Han, J., Olalotiti-Lawal, F., & Datta-Gupta, A.

citation count

  • 5

publication date

  • May 2017