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The significance of accurate permeability prediction cannot be over-emphasized in oil and gas reservoir characterization. Support vector machine regression (SVR), a computational intelligence technique, has been very successful in the estimation of permeability and has been widely deployed due to its unique features. However, careful selection of SVR hyper-parameters is highly essential to its optimum performance and this task is traditionally done using trial and error approach (TE-SVR) which takes a lot of time and do not guarantee optimal selection of the hyper-parameters. In this work, the performance of particle swarm optimization (PSO) technique, a heuristic optimization technique, is investigated for the optimal selection of SVR hyper-parameters for the first time in modelling and characterization of hydrocarbon reservoir. The technique is capable of automatic selection of the optimum combination of SVR …