P-wave velocity prediction via seismic data using seismic attributes

the most important output of seismic data (especially 3D seismic data ) interpretation is shape of the hydrocarbon reservoir structure. This is yet more important in south and south -west iran reservoirs where hydrocarbon accumulation is mostly under control of structural factor. although maps of target horizons are usually build by means of interpretation of seismic data in time domain , in order to use these maps in new well designing process , time to depth conversion of seismic data and interpreted horizons is obligatory. the best pieces of informaiton which can be utilized in time to depth conversion of seismic data are: tomography resulted velocity model , acoustic impedance inversion resulted velocity model , and stacking velocity model . but there is another new course of action when the aforementioned pieces of informaiton are not available where we can use the incorporation of wave velocity well log sinic log and seismic data in prediction of velocity model. in this study , after correlating synthetic trace and seismic data on well locations which leaded to correction of available check -shot , firstly by appliction of multi -variant linear regerssion and multi -layer feed forward neural network MLFN , relation between measured P-wave velocity in 5 wells and seismic attributes in a south -west iranian reservoir was explotied then by the appliction of cross validation, correlation between measured data and exploited one were examined and this coefficients for multi variant linear regression and multi -layer feed forward neural network were 76 and 83.5 percent respectively.