The Ss oil field is found in the Turpan-Hami Basin’s Taipei Sag’s arc structural belt. This reservoir has a complicated character that has a significant impact on reservoir modeling and production prediction. This is a fault-block reservoir with ultralow permeability and low porosity that is divided by 57 faults. A static model was constructed by Petrel software based on reinterpretation of original log and core data and seismic information so as to clarify the spatial distribution of oil and water in the reservoir and to fit the development history of the later simulated reservoir. The integrated geological modeling approach is described in this work using the Ss reservoir as an example. A 3D structural model was built based on the spatial cutting relationship between the layer model and the fault, and the model’s quality was improved by breakpoint data, which more correctly depicted the structural properties of the research area. The lithofacies model was built within the restrictions of sedimentary facies using the sequential Gaussian simulation (SGS) stochastic modeling approach, which is paired with variogram data analysis to achieve the range value. To obtain the porosity and permeability model, the empirical formula of porosity and permeability, the SGS method, and the variation range value was input into the lithofacies model. It is important to note that the input lithofacies and property models have values of the same range. To gain the water saturation model, the distinct S w function formulas of the S 1 ~ S 4 layer derived from the J S w function were fed into the software. The NTG model was created according to the lower limit of porosity, which is 11%. The merging of detailed reservoir description and simulation led to the establishment of the Ss reservoir geological model. In the plane, the scale of the geological model has reached the meter level and decimeter level in the longitudinal direction. It also offers a framework for optimum reservoir modeling for complex fault-block reservoirs. This method improves the accuracy and precision of the model by reflecting the reservoir’s heterogeneity and the oil-water distribution. It could provide more details for future reservoir research such as fine reservoir simulation.