Reliable evaluation of the petrophysical properties of the reservoir is necessary to evaluate and characterize the reservoir layers, determine the quantity of hydrocarbon reserves and production. Several factors (such as, type of porosity, size and type of rock holes) are factors in determining the petrophysical characteristics of the formation. Static petrophysical properties including porosity, lithology, water saturation and volume concentration of shale. While dynamic petrophysical properties including absolute permeability, mobile hydrocarbon saturation and relative permeability depending on saturation and capillary pressure. Advanced logging technology such as acoustic logs, density logs, image logs, etc. helps to identify various petrophysical parameters of the reservoir. Examining the information obtained from petrophysical logs is effective and important in determining the quantity and evaluation of hydrocarbon reserves. The main goal of this research is to evaluate and build a static model of the studied reservoir and to specify the reservoir layers by calculating and determining petrophysical indicators (porosity, shale volume, permeability, etc.) with the help of well logging data (neutron logs, density, acoustic, depth, Gamma, resistance, etc.) and using Geolog software. In addition, water saturation data was modeled using machine learning methods according to different logs. The investigated depth was from 4050.6 to 4560 meters. Each of the images used in this research includes more than 3000 data in the desired depth. During the investigations, the main lithology of the formation under study is mainly composed of limestone and a small amount of shale. By building a static model and determining the conditions (cut offs) for the obtained parameters, the productive zones and reservoir have been determined. After final analysis and zoning, layer 3 has the highest average net porosity (about 18%) with an average net water saturation of about 17%. Secondary