Porosity is one of the most important properties of oil and gas reservoirs． The porosity data that come from well log are only available at well points． It is necessary to use other method to estimate reservoir porosity． Seismic data contain abundant lithological information． Because there are inherent correlations between reservoir property and seismic data，it is possible to estimate reservoir porosity by using seismic data and attributes． Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets． It has been used to extract the mathematical relation between porosity and seismic attributes． Firstly， a seismic imped- ance volume is calculated by seismic inversion． Secondly， several appropriate seismic attributes are extracted by using multi- regression analysis． Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes． Finally，this trained probabilistic neural network model is im- plemented to calculate a porosity data volume． This methodology could be utilized to find advantageous areas at the early stage of exploration． It is also helpful for the establishment of a reservoir model at the stage of reservoir development．