A New Method for Quantitative Diagenesis via Digital Rock Tools

Abstract

Abstract The quantitative study of cementation plays a critical role in characterizing sedimentary rocks, with significant implications for geology, petroleum engineering, and environmental science. By understanding the evolution processes of cementation, researchers can enhance the interpretation of diagenesis in reservoir rocks and accurately quantify the properties influencing the displacement of hydrocarbons. Accurate quantification of reservoir rock properties is essential for developing reservoir models, particularly for heterogeneous rocks. Furthermore, understanding the pore system that controls hydrocarbon or CO2 flow in reservoir rocks is crucial for predicting hydrocarbon displacement and CO2 storage efficiency. Therefore, a quantitative method is required to gain a comprehensive understanding of the diagenesis of reservoir rocks and their pore structure. This study aims to use the detailed pore structure and diagenesis information from high-resolution scanning electron microscopy (SEM) imaging to quantify the diagenesis linked to the reservoir rocks’ quality. The methodology involves categorizing rock samples into three different classes based on the quantification of pore and grain size distribution and cement spatial distribution features. The North Sea Oil Field data is used as a case study. Here, it also presents a quantitative approach for classifying pore, grain, and cement features using gray-value threshold segmentation. The method consists of two steps. First, the quantitative cement features are classified. Then, we link the diagenesis process with these quantitative cement features, enabling the evaluation of diagenesis in sedimentary rocks and its impact on hydrocarbon displacement and CO2 storage efficiency. The results of this characterization method demonstrate its effectiveness in distinguishing and quantifying pore, grain, and cement distributions. Moreover, it establishes a connection with lithofacies and well logging features. In summary, our study highlights the importance of quantifying cementation in sedimentary rocks for various engineering and scientific disciplines. By utilizing high-resolution SEM imaging and employing the gray-value threshold segmentation method, we successfully classify and quantify pore, grain, and cement features. The findings have significant implications for the development of accurate reservoir models and improved resource management.