The hazards in complex process systems evolve at an accelerated rate. It is extremely difficult if not impossible to identify and assess all potential hazards and develop strategies to manage them. This demands next generation of process system that is, intelligent to learn faults and prevent them from further propagating, adaptive to evolving conditions, and quick to recover in case failures take place in a component of part of the system. Resilience engineering is a comprehensive term that captures these three (absorptive, adaptive, and recovery) important characteristics of a system. There are limited tools to qualify or quantify the resilience of a system. There have been hardly any studies conducted on dynamic resilience assessment. This paper proposes a dynamic approach to quantify resilience under varying conditions. The approach uses Stochastic Petri-nets (SPN) coupled with Monte Carlo simulation to model and analyze resilience metrics. The proposed approach is tested on a crude oil pipeline system. The test results demonstrate a clear understanding of the resilience characteristics of the system and its evolving nature. This work puts forward a clear pathway for an integrated dynamic model for resilience engineering.