Proxy models are becoming more widely used as they can simplify highly complex processes with reasonable accuracy. Process such as production history matching, well placement, risk analysis, for example, generally involve large number of reservoir simulations and large computational effort. To minimize this problem some techniques such as Spline, Neural Networks, Kriging and Experimental Design, have been presented in the literature to be used as proxies to reservoir simulator. Due to the importance of the decisions related to the development and management of petroleum fields, the development of proxies with high accuracy can be a decisive aspect in a project. The successful applications of Neural Networks in several research fields suggest the investigation of appropriated architectures to be used as proxies to reservoir simulator. In this article novel proxies to reservoir simulator, based in Neuro-Simulation Techniques, are presented. These proxies present a high accuracy to reservoir simulator. Five different architectures of Neural Networks were studied and applied in two case studies related to history matching. The results obtained showed the large potential of application of the techniques introduced.