P. Kianoush - Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Gh. Mohammadi - Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Seyed A.K. Hosseini - Department of Petroleum, Materials and Mining Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
N. Keshavazr Faraj Khah - Deputy Manager Geoscience Faculty, Research Institute of Petroleum Industry (RIPI), Tehran, Iran
P. Afzal - Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

In the seismic methods, estimation of the formation pressures is obtained by converting the seismic velocity to the pore pressure, and comparing it with the effective pressure during the well-test program. This work is a new challenge regarding the velocity study domain in an oil field in SW Iran. The reservoir generally consists of carbonate rocks, and contains no shale interbeds. Here, ۲۳ well information, seismic data interpretation, compressional (Vp), and shear velocity (Vs) models are implemented. The models are determined from the combined geo-statistical methods, and the results obtained are compared with the fractal models. The final Vs cube is modeled in order to determine the formation fracture pressure using the exploratory well cores and dipole sonic imager (DSI) Vs logs with a correlation coefficient of ۰.۹۵ for the Vs data obtained from the porosity, lithology, and primary DSI data. The vertical seismic profiling (VSP) data introduce a maximum interval velocity of ۲۷۶۰-۲۹۰۰ m/s in the field related to the Gotnia formation. The final amounts ​​of seismic acoustic impedance inversion (AI) at the bottom of the field are mostly in the range of ۸۰۰۰-۱۵۰۰۰ [(m/s)*(g/cm۳)], which can be related to the calcareous formations. Based on the Logratio matrix obtained from the fractal velocity-volume (Vp-V) model, the maximum overall accuracy (OA) in the dominant limestone intervals is ۰.۷۴. It indicates a high correlation of the Vp cube model obtained from the combination of sequential Gaussian simulation (SGS) and co-kriging models with AI. The uncertainty studies of Vp model in blind wells are about ۵۰%, which is acceptable considering the large well numbers.

Seismic velocity modeling, Compressional velocity cube, Acoustic impedance inversion, Formation pressure, Velocity-volume (Vp-V) fractal model