Process systems in petroleum industries such as oil refining systems have been becoming increasingly large and automatic. There can be strong interdependencies between various facilities and components. To reduce the probability and mitigate consequences of equipment failures, these interdependencies have to be assessed. The objective of this paper is to present a systematic modeling method for representing the interdependencies of process infrastructures in petroleum industries, as part of the risk early warning system. The proposed systematic modeling method involves several modeling steps. Firstly, Multilevel Flow Modeling (MFM) is used to represent a system in terms of goals, objectives, functions and components, each of which can be described at different levels of part-whole decomposition. Secondly, HAZOP study is carried out based on the MFM, by which all of the possible deviations and their corresponding potential fault causes and consequences are analyzed carefully. Thirdly, dynamic Bayesian Network (DBN) is used to build the fault causal relationships that represent the fault interdependencies in the complex system. Finally, by the inference mechanism of DBN, the most possible initial reason(s) happened in the fault interdependency network with multi-reasons and consequences can be found accurately for risk early warning. Examples from a case study are included to illustrate the effectiveness and accuracy of the approach.