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Multi-stage, multi-swarm PSO for joint optimization of well placement and control

Evolutionary optimization algorithms, including particle swarm optimization (PSO), have been successfully applied in oil industry for production planning and control. Such optimization studies are quite challenging due to large number of decision variables, production scenarios, and subsurface uncertainties. In this work, a multi-stage, multi-swarm PSO (MS2PSO) is proposed to fix certain issues with canonical PSO algorithm such as premature convergence, excessive influence of global best solution, and oscillation. Multiple experiments are conducted using Olympus benchmark to compare the efficacy of algorithms. Canonical PSO hyperparameters are first tuned to prioritize exploration in early phase and exploitation in late phase. Next, a two-stage multi-swarm PSO (2SPSO) is used where multiple-swarms of the first stage collapse into a single swarm in the second stage. Finally, MS2PSO with multiple stages and multiple swarms is used in which swarms recursively collapse after each stage. Multiple swarm strategy ensures that diversity is retained within the population and multiple modes are explored. Staging ensures that local optima found during initial stage does not lead to premature convergence. Optimization test case comprises of 90 control variables and a twenty year period of flow simulation. It is observed that different algorithm designs have their own benefits and drawbacks. Multiple swarms and stages help algorithm to move away from local optima, but at the same time they may also necessitate larger number of iterations for convergence. Both 2SPSO and MS2PSO are found to be helpful for problems with high dimensions and multiple modes where greater degree of exploration is desired.

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