Multi-stage, multi-swarm PSO for joint optimization of well placement
and control
- URL: http://arxiv.org/abs/2106.01146v1
- Date: Wed, 2 Jun 2021 13:34:50 GMT
- Title: Multi-stage, multi-swarm PSO for joint optimization of well placement
and control
- Authors: Ajitabh Kumar
- Abstract summary: This work proposes a multi-stage, multi-swarm PSO (MS2PSO) to fix certain issues with canonical PSO algorithm.
Multiple experiments are conducted using Olympus benchmark to compare the efficacy of algorithms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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