To select appropriate mud weight and also for proper casing design, we should estimate Pore pressure prior to well drilling. In a well control operation, there is a need for urgent instructions to make an immediate and principled decision during the kick (influx). In addition, due to the relatively high cost of directly measuring the pore pressure, it is economically feasible to use low cost methods that provide a continuous spectrum of pore pressure data. Realizing the importance of the pore pressure and period of extensive research, researchers and engineers have completed major projects in this regard. Borrowing the findings of Hottman, Johnson, Eaton, and Bowers in a ltr" Repeat formation test is the direct way of finding the amount of pore pressure although by using seismic and well-logging data we could able to estimate it indirectly. The main g of this project was to use RFT testing information to determine the pore pressure. For this purpose, the pore pressure of the new wells was estimated by using the neural network according to available information in the other wells drilled in the same field. At this stage, the pore pressure values at each well were determined continuously using MATLAB software and then evaluated with pore pressure data and an equation was assigned to it. In the first method, we initially assumed that the process of pore pressure changes in this field locally, the results indicate that the pressure variations in this method are linear and they have huge discrepancies with actual data. In the second method, the pressure variations in the selected wells were obtained in the form of partial and nonlinear changes. The results of the second method obtained with high accuracy where the correlation coefficient calculated with an average of 88% for the three-layer neural network. In the end, the results of both stages were compared together