Evaluation of the pore structure of tight sandstone reservoirs based on multifractal analysis: a case study from the Kepingtage Formation in the Shuntuoguole uplift, Tarim Basin, NW China

摘要

Both the microscopic pores and the pore structure of a reservoir affect its properties, including permeability and the distribution of oil and gas. Due to the stronger pore structure heterogeneity in tight sandstone reservoirs, it is difficult to characterize the pore structure of tight sandstone with traditional Euclidean geometry. In this study, based on the comprehensive utilization of reservoir data, such as petrophysical property data, mercury injection capillary pressure data and thin sections images, image processing and multifractal analyses are used to classify and describe the pore structure of tight sandstone in the Kepingtage Formation in the Shuntuoguole low uplift, Tarim Basin. The relationships among pore structure parameters, physical properties, and multifractal feature parameters are discussed in detail. Finally, a pore structure classification method using multifractal parameters based on K-means clustering analysis is proposed and applied to tight sandstone. The results show that the pore structure of the Kepingtage Formation can be divided into four types with distinct multifractal characteristics. The multifractal parameters correlate well with the porosity and permeability. The multifractal parameters alpha(min), alpha(max), D-min, and D-max are positively correlated with the average pore throat radius (r(t)) but are negatively correlated with displacement pressure (P-d). The parameters alpha(min), alpha(max), D-min, and D-max are the most sensitive indicators of pore structure and can serve as good indicators of the pore structure type. This pore structure classification method based on multifractal analysis and K-means clustering can be effectively used to classify the pore structure of tight sandstones. This study provides an effective means of pore structure classification and heterogeneity characterization based on thin sections, which is especially valuable in regions without high-pressure mercury injection data.