Abstract
Practical production optimization often involves determining a large number of well control settings such that an objective function is maximized or minimized. When derivative-free population-based optimization algorithms are applied for these high-dimensional problems, inefficient performance or slow convergence can be observed. In this work, we develop a multigroup optimization strategy to mitigate these issues. The first step of the method entails the ranking of decision variables based on their statistical significance, as observed during a short preprocessing optimization run. Variable groupings are then established. Group-by-group optimization, in which only a subset of decision variables is considered at each stage, is then conducted. As a final step, additional (limited) iterations with the full set of variables are performed. Particle swarm optimization (PSO), a global stochastic search method, is applied in this study, though the multigroup treatment is compatible with a range of algorithms. Detailed performance comparisons between multigroup strategies and standard optimization are presented for a model based on the 3D Brugge case. The problem involves 64 wells and 320 decision variables. The multigroup optimization framework includes several algorithmic parameters, and the use of different settings for these is shown to provide a range of promising procedures. Specifically, one such multigroup treatment requires 72% of the flow simulations of standard optimization, but it provides a final net present value (NPV) that is 2.6% higher than that from standard optimization. Another procedure provides essentially the same NPV as standard optimization but with only 40% of the flow simulations. The multigroup framework developed in this study can be extended to treat other challenging high-dimensional problems such as field development optimization.
NSTL