The prompt and effective evacuation of laboratory in high-risk areas is an important part of disaster preparation and safety management. In this paper an automated GIS-based evacuation modeling framework for application to the support of risk-informed emergency planning in indoor environments is presented, specifically for the case of petroleum laboratories at KFUPM. The method transforms CAD-generated floor plans into indoor spatial networks with structure, allowing the automated generation of evacuation routes with Python-based shortest-path algorithms. Travel distance and evacuation time are used to quantitatively measure the evacuation performance under specific occupancy conditions. Results indicate that evacuation efficiency is significantly impacted by spatial configuration, and that location of the laboratory in the space has a significant effect on longer evacuation times. The evacuation times under non-panic conditions are estimated and within acceptable ranges and serve as a baseline to assess evacuation performance. The framework adds consistency and scale to the process and decreases the amount of manual mapping. Despite the assumptions made, it is a useful and effective tool for quick evacuation analysis. It can be expanded to handle dynamic situations, building multiple floors and AI decision making for emergency response.