Minimizing supply chain costs is a critical challenge for the successful deployment of full-chain CO₂ capture and storage (CCS) systems, particularly in industrial clusters with diverse source and sink characteristics. This study presents a superstructure optimization model using Mixed-Integer Linear Programming (MILP), focusing on a case study in South Sumatra, Indonesia. The model’s key innovation is the integration of time-dependent reservoir injectivity profiles, derived from well simulations and decline analysis, directly into the MILP framework. This allows the model to constrain injection timing and capacity, accurately reflecting reservoir availability over a 30-year planning horizon. The model evaluates multiple network configurations, including single-well and multi-well systems, allowing for coordinated injection scheduling. The results highlight the critical role of Hub-based infrastructure, where optimized configurations reduce transportation costs by approximately 34%, from an average of $25.36 to $16.72 per ton, through economies of scale and shared infrastructure. Multi-well systems further improve storage capacity and injection efficiency, particularly when injection schedules are carefully optimized. By strategically managing injection capacity and scheduling reservoir availability, the system achieves additional cost reductions, with transportation costs ranging from $15.89 to $16.56 per ton. The findings highlight the critical role of superstructure-based designs in optimizing the interactions among sources, sinks, and transportation networks. Coordinated injection strategies and infrastructure planning are essential to reducing CCS costs and enhancing scalability. This study underscores the value of systematic optimization in achieving cost-effective and scalable carbon storage solutions across diverse industrial contexts. © The Author(s) 2025.