Study on offshore sandstone reservoir classification method based on cuttings digitization technology

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Abstract Reservoir classification research is of great significance for improving the accuracy of oil and gas exploration and development, reducing risks, and enhancing economic benefits. Traditional reservoir classification methods are primarily based on macroscopic geology and core analysis, lacking descriptions of reservoir pore structure characteristics and microscopic geological features, with very limited data dimensions obtained. This study uses sandstone cuttings samples from a well in an offshore sandstone reservoir as an example and employs digital core technology to conduct reservoir classification research. First, high-resolution micro-CT scanning was performed, and the microscopic lithological characteristics of different blocks were observed based on the scanned images. The diagenesis and particle contact relationships were qualitatively analyzed. Next, digital methods were used to quantitatively analyze pore structure characteristic parameters such as porosity, permeability, and connectivity. Then, the qualitative analysis results of the images were combined with the quantitative analysis results of the digital cuttings to form a set of classification evaluation standards for a certain sandstone reservoir in the eastern South China Sea. Finally, based on the characteristic parameters obtained from the digital quantitative analysis of the cuttings and the on-site pressure-flow data, a big data analysis method was used to screen out the reservoir characteristic parameters with high correlation, providing data support for the rapid evaluation of reservoir mobility. The reservoir classification evaluation standards and flow prediction models formed in this study can provide technical support for accurately formulating reservoir development plans in oil fields in the eastern South China Sea and have significant reference value for the fine evaluation of similar reservoirs. In addition, reservoir classification methods based on digital core analysis exhibit efficiency and multidimensionality, which are crucial for well placement selection, wellbore design, stability assessment of well walls during drilling, interpretation of logging data, and accurate prediction of production capacity.