Abstarct: The spatial distribution of petrophysical parameters in a hydrocarbon reservoir is one of the most important effective factors for descxription of the reservoir properties. The reservoir modeling is an integrated engineering and geological comprehensive modeling. A comprehensive reservoir model requires knowledge of geology, rock and fluid properties, fluid flow in the reservoir, mechanism of production, drilling and completion, and so on. In order to characterize the reservoir, well log and seismic data are usually used. Well data are sufficiently accurate but they cover a limited space of the reservior. Considering the geological complexity of the reservior, the well data or information can not simply be extrapolated to the entire reservior. On the other hand, seismic data are less accurate but cover a large area of the reservoir. In this study, first, the porosity has been modeled in the reservoir zone of the study wells using well data and seismic information with the help of geostatistical methods by the Petrel software, and also, with the help of the method of artificial neural networks (ANNs) by the Hampson Rusell suite (HRS) software. Then, the results of these two methods have been compared. For the development of hydrocarbon fields, a more and better comprehensive study and management of the reservoir is essential. One of useful methods in the classification of reservoir rocks and separation of productive and non-productive zones is the introduction of the reservoir zones baxsed on the concept of hydraulic flow units using cluster analysis that presents a new concept to define the rock types and the reservoir zones, and removes the disadvantages of geological and petrophysical common zoning. The hydraulic flow unit is a specific volume of reservior in which geological and petrophysical properties of the reservoir are the same. Accordingly, the accurate prediction of hydraulic flow units in a reservoir is a major task to achieve reliable petrophysical descxriptions of the reservoir. There is normally no simple relationship between porosity and permeability. Thus, determination of hydraulic flow units is a suitable method for evaluation of permeability-porosity relationship in a reservoir. In this research, to present permeability-porosity relationship, we, first, determine hydraulic flow units using the methods of flow zone indicator (FZI), electrofacies and reservoir quality index (RQI) in three wells of the study hydrocarbon field, and then, obtain and analyze permeability-porosity relationship in each of the hydraulic flow units. The results of this study indicate the equal number of hydraulic flow units in both FZI and electrofacies methods, but number of hydraulic flow units in RQI method is more than the other two methods. Furthermore, the number of hydraulic flow units in one of wells is different from the other two wells. In addition, the results obtained from investigation of permeability-porosity relationship using the above-mentioned methods in the hydrocarbon field indicate that the FZI method establishes this relationship with a higher correlation coefficient compared to the electrofacies and RQI methods. Moreover, determination of hydraulic flow units from well logging data using the electrofacies method is possible even if the porosity and permeability date are not available. Since one of the most important steps in reservoir studies is to accurately compute petrophysical parameters, and also, to reduce their computation errors, the measurement accuracy of well logging tools and the existence of uncertainty in the results are important factor in increasing the accuracy of the computation of petrophysical parameters. In final stage of this study, the reservoir petrophysical parameters in one of the wells of the study field have been calculated using deterministic method. The results have shown high reservoir quality in the well. Then, the uncertainties of the computed petrophysical logs using Monte Carlo analysis have been calculated. Eventually, using the uncertainty values of the petrophysical logs in the study reservoir, petrophysical parameters have been calculated using Monte Carlo analysis and proved (1P), probable (2P) and possible (3P) values have been estimated for each of these petrophysical parameters. Using the differences between these values, the uncertainty in each petrophysical parameter has been investigated, and baxsed on this investigation, the maximum and minimum amounts of uncertainty have been obtained that are related to permeability and porosity parameters, respectively.