Rock Physical Modeling of Tight Sandstones Based on Digital Rocks and Reservoir Porosity Prediction From Seismic Data - Guo, Wei; Dong, Chunmei; Lin, Chengyan; Wu, Yuqi; Zhang, Xianguo; Liu, Jinshuai (Frontiers in Earth Science, 2022)
Digital rock physics (DRP) has become an important tool to analyze the characteristics of pore structures and minerals and reveal the relationships between microscopic structures and the physical properties of reservoirs. However, it is greatly difficult to upscale the rock physical parameters, such as P-wave velocity, S-wave velocity, and elastic moduli, from DRP to large-scale boreholes and reservoirs. On the other hand, theoretical rock physical modeling can establish the internal relationship between elastic properties and physical parameters of tight sandstones, which provides a theoretical basis for seismic inversion and seismic forward modeling. Therefore, the combination of digital rock physics and rock physical modeling can guide the identification and evaluation of gas reservoirs ‘sweet spot’. In this study, the CT images are utilized to analyze the mineral and pore characteristics. After that, the V-R-H model is used to calculate the equivalent elastic moduli of rock only containing the mineral matrix, and then the differential equivalent medium (DEM) model is employed to obtain the elastic moduli of dry rocks containing minerals and pores. Subsequently, the homogeneous saturation model is utilized to fill the fluids in the pores and the Gassmann equation is used to calculate the equivalent elastic moduli of the saturated rock to accurately estimate the equivalent elastic moduli of the saturated rock of tight sandstones. The rock physical modeling is calibrated and the reliability of the rock physical model is verified by comparing those with the logging data. Afterward, the empirical relationship of the rock porosity established from CT images and rock elastic moduli is obtained, and then the elastic parameters obtained by seismic data inversion are converted into porosity parameters by using this empirical relationship. Finally, the porosity prediction of large-scale reservoirs in the study area is realized to figure out the distribution of gas reservoirs with high porosity. The results show that the H3b and H3c sections of the study area exhibit higher porosity than H3a. For the H3b reservoir, the northeast and middle areas of the gas field are potential targets since their porosity is larger than others, from 10% to 20%.