###### Abstract
[12] evaluated the effect of new gas solubility and bubble point pressure models on PVT parameters and optimized the gas injection rate in gas-lift dual gradient drilling, which is crucial for improving the production efficiency of oil and gas reservoirs. [PERSON] et al. [13] reviewed the influence of components on the performance of PVT modules, providing a reference for optimizing the design of PVT modules. In the prediction of PVT properties, [PERSON] et al. [14] proposed an artificial intelligence-based method for predicting the PVT properties of black oil, demonstrating the application prospect of AI technology in reservoir fluid analysis. [PERSON] and [PERSON]. [15] studied the PVT modeling of complex heavy oil mixtures, providing support for improving the development efficiency of heavy oil reservoirs. [PERSON] et al. [16] developed new models for predicting the PVT properties of crude oil, which have high prediction accuracy and practicability. In addition, some scholars have proposed new correlation methods [17] and performance comparisons [18] to further optimize and improve the prediction and analysis capabilities of PVT properties. [PERSON] [19] determined the gas-saturated oil density under reservoir conditions and developed quality control indicators for PVT laboratory reports in the study, providing a new tool for the accurate measurement and evaluation of reservoir fluid properties. [PERSON] et al. [20] conducted a thermodynamic analysis, defined the PVT equation of state, and reviewed gas injection along with a case study in three wells of an Iranian oil field. [PERSON] et al. [21] improvedthe PVT analysis of representative fluids in a gas condensate reservoir through integrated characterization and tuning strategies, providing technical support for the efficient development of gas condensate reservoirs. With the continuous development of machine learning, scientific machine learning (SciML) has been applied to solve practical problems in many engineering fields. For example, some scholars have studied the application of machine learning in shipbuilding, ocean, and ocean engineering problems [22]. Therefore, foreign research on the high-pressure physical property analysis of reservoir fluid PVT has made significant progress. Not only have important achievements been made in experimental measurement and theoretical model prediction, but active exploration has also been carried out in the application of machine learning and artificial intelligence technology. These studies have provided a scientific basis and technical support for the effective development and utilization of oil and gas reservoirs as well as new ideas and inspiration for this study on the high-pressure physical property analysis of reservoir fluid PVT based on the graph convolutional network model. In the review of previous work, although many scholars have achieved certain results in the field of the high-pressure physical property analysis of reservoir fluid PVT, there are still limitations. Some studies have relied on laboratory measurements, which are costly and time-consuming. Some prediction methods based on theoretical models have poor adaptability under complex geological conditions and diverse fluid characteristics. Although the application of machine learning and deep learning technologies has brought new opportunities, most existing studies simply applied models and failed to fully explore the complex nonlinear relationships between data. The innovation of this study lies in the first application of the enhanced ChebNet model in this field, breaking through the limitations of traditional methods. Compared with the ordinary ChebNet model, the enhanced model in this study has been optimized in structure and algorithm.