The international environmental monitoring and scientific research communities have stressed the importance of utilizing operational satellites to produce routine fire products for hazards applications and to ensure long-term stable records of fire activity for land-use/land-cover change analyses and global climate change research. Since the year 2000, the GOES Wildfire Automated Biomass Burning Algorithm (WF_ABBA) has been providing diurnal information on fire activity throughout the Western Hemisphere. The Advanced Baseline Imager (ABI) on GOES-R and beyond will enable continued analysis of fire activity with significant improvements in fire detection and sub-pixel fire characterization. UW-Madison CIMSS has modified the current WF_ABBA to take advantage of the enhanced fire monitoring capabilities of ABI. Modifications include updating modules that identify and characterize sub-pixel fire activity. Various GOES-R ABI proxy data sets are used to test the updates. In order to understand the impact of these modifications and to evaluate the performance of the GOES-R ABI WF_ABBA, the algorithm was applied to simulated GOES-R ABI data derived from higher spatial resolution MODIS data and model simulated ABI data. The results demonstrate the robustness of the algorithm to meet GOES-R requirements for fire monitoring.