Abstarct: Reservoir Simulation is an art of using combination of physics, mathematics, reservoir engineering, and computer programming to develop a tool for predicting the performance of a hydrocarbon reservoir under various operating conditions. The necessity of optimizing oil extraction methods is to obtain predictions of hydrocarbon reservoir performance and increase the efficiency of reservoirs under various operating conditions, and it comes from the fact that in a hydrocarbon recovery project that might involve hundreds of millions of dollars in investments, the risk associated with using the best method for the desired outcome must be evaluated and minimized. The main approach of this research is to develop an optimization phase in a simulated model whose main purpose is to find the most optimal simulated static model for improving oil production with a combination of static and dynamic simulation techniques in the water injection process. In this study first, artificial data of porosity, permeability and saturation of oil and viscosity were generated as normal and randomly by MATLAB software. Then these artificial data were used for geostatistical simulation. The method used to static simulation of region is Sequential Gaussian Simulation (SGS) implemented by SGeMS software, and for each of four static parameter, 100 realizations were obtained. The realizations of SGS was randomly entered in the dynamic model and the pressure in each of the blocks in the oil production reservoir were obtained. By comparing these pressures with constant value of 4000 psi, we calculated the Sum of Squared Errors (SSE). Then, assuming that three other realization are fixed, a new realization of permeability was randomly introduced into the model; now, if the new error is greater than the error of the model previously obtained, then other realizations of permeability are invoked, otherwise the previous model will be replaced by optimal one and proceed to other realizations. This process continues until the most optimal permeability realization is obtained, and finally, assuming that this optimal realization is fixed, this process will be done for the porosity and saturation of oil and viscosity, in order to ultimately achieve the most optimal response. The dynamic model of the reservoir is coded in the Python software by Slightly-Compressible fluid flow equations in three dimensions and Finite- Difference Approximation for linear flow equations. Finally, with a reliable static model, we can look at different locations for designing injection and oil production wells, potential intelligent wells areas, or optimizing the management of a reservoir, which reduces costs and, at the same time, increases oil production.