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Abstract Reservoir modelling is frequently used in the oil industry to measure the risk associated with alternative production scenarios. However, reservoir models themselves contain a high level of uncertainty because of the typically very limited, sparse and multi-scaled reservoir knowledge. The effect of this uncertainty can be assessed by producing a set of diverse models that match the production data reasonably well and using these models to quantify uncertainty in predicting the future performance of the reservoir. Evolutionary and swarm intelligence algorithms have become very popular for history matching due to their simplicity and parallel implementation capacity. This paper focuses on the application of Particle Swarm Optimisation (PSO) for history matching the Brugge field (a recent SPE benchmark case study). The parameterisation of the model is based on principal component analysis (PCA) for modelling spatially correlated random fields (e.g. porosity, net-to-gross and permeability) applied to the set of initial realisations which describe the range of prior beliefs. The PSO is then used to find the set of possible combinations of parameters, represented by the PCA eigenvalues, which match the historical data and honour the static data from the wells present in the initial realisations. We show that PSO is able to find multiple good and diverse history matched models for the Brugge reservoir without exhaustive sampling of the parameter space. Uncertainty of production forecasts are quantified by P10-P50-P90 uncertainty envelope obtained from the ensemble of PSO models. The history matching results are compared with the ones obtained with Ensemble Kalman Filter data assimilation method. These results show the ability of PSO to handle large history matching problems and obtain results comparable to the EnKF for this case study.