非电阻率测井流体识别研究现状及展望OACSTPCD

Currently resistivity logging is the primary method for fluid identification. However, accurately identifying fluid properties in formations with low porosity and permeability, significant reservoir heterogeneity, and unique mineral development poses challenges. To address this issue in complex reservoirs, some non-resistivity logging-based methods have been developed to identify fluid properties. This paper describes the principles, application examples, and adaptation conditions of these methods. The following viewpoints are proposed regarding the development direction of non-resistivity logging fluid identification methods: optimizing the two-dimensional nuclear magnetic resonance fluid identification plate by combining nuclear magnetic resonance experiments and developing three-dimensional nuclear magnetic fluid identification methods based on the improving of data quality and processing effectiveness. Directly inverting fluid characterization parameters using Stoneley waves. Conducting substantial experiments and simulations to improve the theory of chlorine yield correction and form a stable chloride ion fluid identification method. Meanwhile, trying to extract fast neutron cross-sections for fluid identification based on nuclear logging tools such as Litho Scanner. Combining machine learning algorithms for extracting fluid information and distinguishing properties from spectra. Integrating various logging techniques to determine fluid characteristics and forming the valuable workflow.