A new method for logging identification of fluid properties in tight sandstone gas reservoirs based on gray correlation weight analysis — A case study of the Middle Jurassic Shaximiao Formation on the eastern slope of the Western Sichuan Depression, China

Abstract

Energy exploration is becoming increasingly complex worldwide, and tight sandstone gas is an important field for the future development of the oil and gas industry. For the reservoir properties of the Shaximiao Gas Reservoir on the eastern slope of the Western Sichuan Depression in the Sichuan Basin, western China, it was found that the low-resistance characteristics of the reservoir complicate the gray characteristics among reservoir fluid property parameters. Some commonly used fluid property identification techniques, such as the flow zone index method, correlation analysis method of logging parameters, and traditional mathematical statistical methods, have poor fluid property evaluation results. Therefore, how to eliminate the influence of the gray features among the reservoir parameters on the identification of reservoir fluid properties and how to accurately identify the reservoir fluid properties are urgent problems that need to be solved. In this paper, we have developed a new method for identifying the fluid properties of tight sandstone reservoirs by combining gray system theory and multivariate statistical theory. This method can perform gray correlation weight analysis on parameters (combined parameters) closely related to fluid properties; furthermore, the logging identification method based on gray correlation weight analysis is used to identify reservoir fluid properties. The results indicate that the gray correlation weight analysis can accurately characterize the gray characteristics of reservoir fluid parameters and that the gray comprehensive correlation weight results are in good agreement with the production status of the studied gas reservoir. We used the method to identify the fluid properties of the target layer in 58 wells in the study area, and the discrimination rate of the model was 86.5%. In addition, the new model was used to predict the reservoir fluid properties of 12 newly drilled wells in the study area and the accuracy of the reservoir fluid property prediction was 91.67%.