Intelligent pore structure classification method from well logs in heterogeneous tight sandstone reservoirs

Tight sandstone gas reservoir, a significant resource in hydrocarbon exploration, is characterized by low porosity, low permeability, strong heterogeneity, and complex pore structures. The log interpretation and reservoir evaluation are facing challenges. Current pore structure evaluation methods from well logs are limited, new log interpretation methods require investigation. Mercury injection capillary pressure (MICP) experiment serves as an effective means to characterize pore-throat structure and flow capacity, and nuclear magnetic resonance (NMR) can describe the distribution of different pore components. Combining these two methods, the pore structure of tight sandstone reservoir is obtained. Based on core MICP and NMR experiments, six parameters sensitive to pore structure are extracted, relationships between pore structure parameters and NMR component porosity are established, and the pore structure evolution from NMR log interpretation is achieved. For pore structure classification, K-means clustering algorithm, principal component analysis, and dimensionality reduction techniques are investigated from pore structure parameters of log interpretation. The above methods are applied to the Yanghugou Formation in the Weizhou area of the Ordos Basin. The pore structure classification was completed. Some case studies indicate that these methods improve the accuracy of pore structure classification and are suitable for other low-porosity, low-permeability tight sandstone reservoirs.