The reliability of petroleum offshore platform systems affects human safety and well-being; hence, it should be considered in plant design and operation in order to determine its effect on human fatality risk. Methane Vapour Cloud Explosions (VCE) in offshore platforms are known to be one of the fatal potential accidents that can be attributed to failure in plant safety systems. Traditional Quantitative Risk Analysis (QRA) lacks in providing microlevel risk assessment studies and are unable to update risk with the passage of time. This study proposes a grid-based dynamic risk analysis framework for analysing the effect of VCEs on the risk of human fatality in an offshore platform. Flame Acceleration Simulator (FLACS), which is a Computational Fluid Dynamics (CFD) software, is used to model VCEs, taking into account different wind and leakage conditions. To estimate the dynamic risk, Bayesian Inference (BI) is utilised using Accident Sequence Precursor (ASP) data. The proposed framework offers the advantage of facilitating microlevel risk analysis by utilising a grid-based approach and providing grid-by-grid risk mapping. Increasing the wind speed (from 3 to 7 m/s) resulted in maximum increase of 21% in risk values. Furthermore, the integration of BI with FLACS in the grid-based framework effectively estimates risk as a function of time and space; the dynamic risk analysis revealed up to 68% increase in human fatality risk recorded from year one to year five.