Dolomite stratum algal dolomite identification method and device

Abstract

The invention provides a dolomite stratum algal dolomite identification method and device. The method comprises the steps of obtaining logging data of a target stratum and core information of algal dolomite; normalizing the interval transit time, the natural gamma and the deep lateral resistivity in the logging data; constructing identification parameters according to the normalized natural gamma, the normalized deep lateral resistivity and the normalized interval transit time; identifying the algal dolomite of the target stratum based on the core information and the identification parameters, wherein the identification parameters are constructed by adopting the natural gamma, deep lateral resistivity and interval transit time, the difference can be amplified in the index level, the characteristics of the algal dolomite are fully highlighted, the algal dolomite can be distinguished more clearly, and the change characteristics of the identification parameters GFL are more obvious, so that the purpose of quickly identifying the algal dolomite is achieved.

Description

technical field [0001] The invention relates to the technical field of petroleum geological exploration and logging interpretation, in particular to a method and device for identifying algal dolomite in dolomite formations. Background technique [0002] Using logging data to identify algal dolomite has always been a worldwide problem. Algal dolomite is a kind of microbial carbonate rock. Because the dolomite formation is transformed by sedimentation and diagenesis (dolomitization, dissolution, fracture development, etc.) , coupled with the role of microorganisms, make the logging characteristics very complex, and it is difficult to use logging to identify different types of microbial rocks, especially the accurate identification of algal dolomite. [0003] Neural network, cluster analysis, discriminant analysis and other identification techniques are mainly aimed at the coarser lithology classification at the level of rock mineral components (Liu Weifu, fuzzy mathematics ide...

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Problems solved by technology

[0002] Using logging data to identify algal dolomite has always been a worldwide problem. Algal dolomite is a kind of microbial carbonate rock. Because the dolomite formation is transformed by sedimentation and diagenesis (dolomitization, dissolution, fracture development, etc.) , coupled with the role of microorganisms, makes the logging characteristics very complex, it is difficult to use logging to identify different types of microbial rocks, especially the accurate identification of algal dolomite

[0003] Neural network, cluster analysis, discriminant analysis and other identification techniques are mainly aimed at the coarser lithology classification at the level of rock mineral components (Liu Weifu, fuzzy mathematics identification of carbonate lithology in deep buried hills[J], Xinjiang Acta Petroleum Sinica, 2003, 15(3): 35-39), or lithology identification for classification of rock structure components (Liu Hong, Tan Xiucheng, Zhou Yan et al., Complex carbonate rock logging lithofacies based on gray correlation identification [J], Daqing Petroleum Geology and Development, 2008, 27(1): 122-125), but for the lithology of algal dolomite, taking Zhendan Dengying Formation in Sichuan as an example, due to the combination of grain lamination and laminar structure It is not clear, but it cannot be identified by electrical imaging, and the algal dolomite cannot be quickly identified

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