Evaluating Equations of State for CO2-Rich Fluids in CCUS Pipelines: Balancing Accuracy and Computational Efficiency in Flow Assurance

The rapid global deployment of carbon capture, utilization, and storage (CCUS) is critical to achieve net-zero climate targets. A pivotal component of CCUS infrastructure is the reliable, cost effective and efficient transport of CO2-rich fluids through pipelines, which demands accurate flow assurance modeling. Central to this is the selection of an appropriate Equation of State (EoS), which governs predictions of thermodynamic properties and influences both model fidelity and simulation time. However, existing literature primarily emphasizes thermodynamic accuracy in isolation, offering limited guidance for balancing prediction accuracy with computational efficiency, which remains a key concern in real world engineering. This paper evaluates four widely used EoS models—GERG-2008, CPA-Infochem, Peng Robinson 1978 Advanced (PR78A), and CO2 Single Component Module (SCM)—using three real-world CCUS case studies involving various simulations across pipeline and well configurations. GERG-2008, though the benchmark for predictive accuracy, was found to be computationally intensive and occasionally unstable under transient scenarios. SCM showed near-parity in accuracy for high-purity CO2 while delivering the fastest simulation times and superior simulation stability. PR78A offered a balanced alternative when used with tuned binary interaction parameters (BIPs), while CPA showed the largest deviation in predictions but remains useful for conservative design margins. The study introduces a scenario-based EoS selection guideline to assist engineers in choosing the most appropriate model depending on CO2 purity, simulation type, and project phase. This fills a key industry gap by integrating both technical performance and computational practicality allowing for an optimized simulation that is reliable, accurate, and efficient. Future research should explore additional EoS such as SRK and EOS-CG-2021, benchmark alternative tools like Ledaflow and PVTsim Nova, and leverage smarter AI-driven methods for EoS selection and BIP tuning. These advancements could ultimately lead to standardized and intelligent guidelines for robust, efficient, and intelligent flow assurance modeling in CCUS applications, accelerating deployment timelines and supporting climate goals.