A Method for Quantitative Identification Lithology Based on Structural Components of Limestone by Well Logging: A Case Study on Carboniferous KT-II, Tucker Anticline, Caspian Basin

Commercial oil flow was obtained in the Carboniferous KT-II layer by many wells drilled in the Tucker anticline which located in the eastern of Caspian Basin. The KT-II mainly develops intra-platform beach reservoirs, which are characterized by complex lithology, thin layers, and strong heterogeneity. And it difficult to divide the microfacies of individual wells in this area.In this study, 5 categories and 10 subcategories limestone has been divided by well cores, cast liquid photos and scanning electron microscope. The 5 categories are the following such as: bright grainstone, stucco grainstone, particulate stucco limestone, grain-contained stucco limestone, and stucco limestone. The 10 subcategories are named as: Shine-grained limestone, bright-grained foraminiferous limestone, brilliant-grained algal limestone, bright-grained inner-clastic limestone, muddy crystalline algae limestone, mud-based foraminiferal limestone, and mudstone-green crushed ash Rocks, bioclastic micrites, bioclastic micrites, and mudstones. According to the analysis of well log cures and the lithology, the 9 lithology-sensitive curves (the curves are the caliper curve, natural gamma, deep lateral resistivity, shallow lateral resistivity, microsphere focused resistivity, compensated neutron log, density log, acoustic log and PE log) are selected for the quantitative identification of lithology. In the study, the typical logging response characteristics of the target lithologies-the 10 subcategories limestone was set up. The lithologic logging response standards of KT-II layer in Carboniferous were established in the block. And the authors preferred the dynamic clustering analysis method to establish the lithology identification model in the work area. Based on the results of reservoir identification in the Tucker anticline, classification accuracy of this method attained 75%. It has a great auxiliary effect on the identification of sedimentary microfacies. The accuracy of reservoir prediction can be improved with the method and it has a good prospects for application.