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Evolutionary optimization algorithms, including particle swarm optimization(PSO), have been successfully applied in oil industry for production planningand control. Such optimization studies are quite challenging due to largenumber of decision variables, production scenarios, and subsurfaceuncertainties. In this work, a multi-stage, multi-swarm PSO (MS2PSO) isproposed to fix certain issues with canonical PSO algorithm such as prematureconvergence, excessive influence of global best solution, and oscillation.  Multiple experiments are conducted using Olympus benchmark to compare theefficacy of algorithms. Canonical PSO hyperparameters are first tuned toprioritize exploration in early phase and exploitation in late phase. Next, atwo-stage multi-swarm PSO (2SPSO) is used where multiple-swarms of the firststage collapse into a single swarm in the second stage. Finally, MS2PSO withmultiple stages and multiple swarms is used in which swarms recursivelycollapse after each stage. Multiple swarm strategy ensures that diversity isretained within the population and multiple modes are explored. Staging ensuresthat local optima found during initial stage does not lead to prematureconvergence. Optimization test case comprises of 90 control variables and atwenty year period of flow simulation. It is observed that different algorithmdesigns have their own benefits and drawbacks. Multiple swarms and stages helpalgorithm to move away from local optima, but at the same time they may alsonecessitate larger number of iterations for convergence. Both 2SPSO and MS2PSOare found to be helpful for problems with high dimensions and multiple modeswhere greater degree of exploration is desired.

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