History matching optimisation in Bayesian framework is a fairly recent approach to quantify uncertainty in oil industry. Currently some innovative global optimisation approaches such as evolutionary algorithms and swarm intelligence methods have gained popularity for tackling history matching problems.
Particle swarm optimisation (PSO) is a swarm intelligence approach for solving optimisation problems. In this approach particles are moving points in parameter space. The position of a particle is a candidate solution to the optimisation problem. Each particle searches for better positions in parameter space by updating its velocity according to rules originally inspired by behavioural models of the movement of flocks of birds.
Recently the PSO algorithm has shown to be a promising tool for finding acceptable multiple history matched models quickly (Mohamed et al., 2009). However, the algorithm control parameters which balance between exploitation–exploration trade–off are to be studied for history matching problems. In this paper we investigate some basic PSO variants for updating the control parameters using a real–life case study.
It is shown that PSO could be improved by optimising the PSO control parameters. Some variants converge faster to good fitting regions in parameter space leading to a fewer number of reservoir simulation runs though others maintain diversity of the reservoir models better for this reservoir example. This study helps better employing the PSO algorithm for reservoir model history matching and uncertainty quantification.