A typical inverse problem encountered in petroleum industries is referred to as history matching. In the early days, history matching was undertaken by manually changing sensitive reservoir parameters until a reasonable match between observed and simulated pressure and production data are obtained. In recent years, the use of assisted history matching has become prominent among academia and the industry and has significantly shortened the time required for manual history matching. However, assisted history matching requires several time consuming simulation runs which might take days to weeks, especially for large and complex reservoir models. In this paper, we presented a new approach to history matching. The approach employs 3-level fractional factorial design, artificial neural network and inverse solution methods to further reduce the computational time required and improve performance. In the inverse solution method of training a neural network architecture, the training input and output data are set to be historical and reservoir data, respectively. This allows to directly simulate the trained neural network and avoid the use of objective function and optimization algorithm. The efficacy of the developed approach was evaluated using a benchmark reservoir model case study which was originally developed for investigation of three-phase three-dimensional Black-Oil modelling techniques under the 9th SPE comparative study project. The proposed approach has required 27 simulation runs of randomly generated realizations. The historical data was generated by running the true case for a period of 900 days under constraints. The result of the case study has successfully demonstrated the efficacy of the proposed algorithm for history matching. © Research India Publications.