Abstract: Large oil and gas pipeline networks are characterized by multiple source and sink nodes, large spatial span and high degree of coupling between the thermal and hydraulic processes, which lead to great difficulty in modeling. It is pointed out in the Medium and long-term oil and gas pipeline network planning that the intellectualization of pipeline networks is the development direction in the future, and building a hybrid model with clear physical meaning and strong generalization capabilities by combining the mechanism knowledge and the data-driven modeling method is critical to realize the intelligent pipeline networks. Herein, the characteristics of mechanism modeling and data-driven modeling were analyzed, the physical properties of the study object were described collaboratively by integrating the mechanism model and the datadriven model, the internal connection among the site data was fully mined, the evolution laws of the process variations were explored, and finally a high-fidelity hybrid model was established. In addition, various structures of hybrid models and the feasibility of their application in oil and gas pipeline industry was summarized, the strategies of hybrid modeling in various application scenarios were clarified and the direction of research on the mechanism and data based collaborative modeling technology in future was discussed. Further, the research results can provide reference to the construction of intelligent pipeline networks.