Carbon emissions from fossil fuels consumption drive climate change, emphasizing the need to transition to clean energy sources. Despite the promise of subsurface applications like Carbon Capture, Utilization, and Storage (CCUS), geothermal energy, and hydrogen storage, challenges related to high cost, accuracy, economic feasibility, and monitoring persist. Integrating DNA sequencing offers a cost-effective solution to monitoring inter-well communication, but faces three main challenges: the lack of large-scale time-lapse monitoring, the absence of microbial tracers selection, and the oversight of front propagation velocity. To address these challenges, this study proposes a method involving time-lapse analysis using large-scale microbial DNA sequencing over two months, including four rounds of sampling, sequencing, and data analysis. First, the microbial composition of injection samples at different time intervals is analyzed to validate community diversity and sampling rationality. Second, injection and production well samples are compared to select stable microbial tracers during the injection cycle. Finally, principal coordinate analysis of tracer microbial species, considering front propagation velocity, is conducted to determine inter-well communication. The accuracy of the results is verified using the Kalman filter method, achieving the goal of time-lapse, large-scale dynamic monitoring of inter-well communication. The outcome of this study sheds light on reducing uncertainties associated with transitioning geoenergy development, contributing to the promotion of a net-zero emission world.