This paper demonstrates a case study of a hybrid methodology based on the combination of radial basis function neural network and sequential Gaussian simulation. The methodology is demonstrated with an application to modelling the porosity distribution in an oil reservoir of the Lower Tertiary in the north of Dongying depression, Shengli Oilfield, East China. The methodology first uses radial basis function neural networks to estimate the porosity trends (fluvial directions) from high-dimensional data system with both well and seismic data. Gaussian simulation helps to do the local uncertainty analysis for the reservoir model. The final results from the hybrid methodology assure our confidence on the reservoir model both horizontally and vertically. They are realistic and honour the geological rules of the oilfield. The technique is fast and straightforward, and provides an effective computational framework for conditional simulation.