Abstarct: The pore pressure prediction has a great importance in assessing drilling risk potential, determining mud weight and well control. Detailed information on the subsurface laxyers pressure is obtained using well pressure test data (RFT). Nowadays, indirect seismic methods are also rapidly expanding their roles in pore pressure estimation of non-drilled areas. Since the most pore pressure prediction models have been developed for sandstone reservoirs, therefore in this study it is firstly attempt to present new methods for pore prediction and the available models are then also modified so that to have acceptable results in carbonate reservoirs. First of all, by analyzing all effective mechanisms, it was shown that the unbalanced compaction is the main effective mechanism for pore pressure in reservoirs in one of the oil field located in the southwestern part of Iran. To achieve the goals, seismic velocity, acoustic impedance and average frequency were then used in six different methods to predict pore pressure within the study area. Seismic velocities that were estimated directly by inversion of seismic data and also by statistical multi-attribute seismic analysis, used to predict pore pressure. Moreover, the acoustic impedance (AI) and frequency attenuation (FA) data were both used as two different fitting methods with pore pressure (direct) and with effective pressure (indirect) to predict pore pressure of the reservoirs. The verification of cross sections of pore pressure cube of various methods, indicated that the velocity method baxsed on multi-attribute seismic analysis, as well as both AI methods clearly discriminated the Ilam, Sarvak, Bourgan and Fahlyian reservoirs from their top and bottom laxyers in the study area. In addition, the horizontal predicted pore pressure maps of these three methods clearly illustrate the impact of buried channels on lateral changes of pore pressure in Sarvak carbonate reservoir which is the main reservoir within the field of under study. However, the other three methods, namely the inversion velocity method and the two frequency attenuation methods, did not well performance in detecting reservoir laxyers and the effect of buried channels on pore pressure changes. The statistical analysis of the results also show that the determination coefficient (R2) between the predicted pore pressure and the well (RFT) data is different for six methods. The R2, from high to low value respectively related to the methods of; the direct acoustic impedance method with R2 = 0.9103, the method baxsed on pore pressure to average frequency data fitting (direct) with R2 = 0.8737, the indirect acoustic impedance method with R2 = 0.8525, the method baxsed on effective pressure to average frequency data fitting (indirect) with R2 = 08386, the multi-attribute analysis velocity method with R2 = 0.8348 and the inversion velocity method with R2 =0.7792. Furthermore, the standard error (SEx) calculation for these different methods shows that SEx was respectively 28(psi) and 50(psi) for the indirect (fitting of effective stress to AI data) and direct (fitting of AI to pore pressure data) AI methods. It was 29(psi) for the multi-attribute analysis velocity method. These results indicate high accuracy and performance of these three pore predication models for identifying of all oil reservoirs from their top and bottom laxyers. Whereas, the standard error of other three methods including; the direct seismic frequency attenuation (fitting of average frequency to pore pressure data) method is 57(psi), that of the inversion velocity method is 71(psi) and finally that of the indirect seismic frequency attenuation (fitting of average frequency to effective pressure data) method is 75(psi). These results confirm those obtained by of the prepared cross and horizontal pore pressure sections of these methods that indicated they are unable to resolve all oil reservoir laxyers in the study area properly.