RAM STUDY METHODOLOGY PART 2 OF 3

OPTIMISING CO_² VENTING in CCUS FACILITIES

For Carbon Capture, Utilisation, and Storage (CCUS) operations, traditional Reliability, Availability, and Maintainability (RAM) methodologies not necessarily provide the desired outcome. Unlike conventional facilities where the focus is on maximising production, CCUS facilities shift the emphasis toward minimising unused or un-sequestered CO₂ (i.e. limiting CO₂ venting over the system’s lifecycle).

To accurately forecast venting rates and durations, two critical inputs are required:

• The CO₂ production profile that reflects the actual volume of CO₂ available for storage  (see Part 1 of this case study: RAM Study Methodology, addresses considerations when using production profiles).
• The predicted losses (i.e. CO2 venting) may need to be calculated in a post-process for each individual simulation in cases when the CO₂ production profile is based on a RAM simulation by itself. 

Integrated Simulation for Greater Accuracy

In a recent case study, a CCUS facility received its CO₂ as a byproduct from a Liquefied Natural Gas (LNG) plant. To predict the CO₂ production over the system life, the existing LNG supply chain RAM model was executed. But instead of using the LNG RAM model predicted average CO₂ production as a common profile, the study used the CO₂ production of each individual simulation as a varying input profile for each CCUS simulation lifecycle. This approach allowed the researchers to capture the inherent variability and uncertainty in the CO₂ supply, providing a more comprehensive and accurate assessment of the CCUS facility's performance and predicted CO₂ capture and venting rates over its lifetime.

This integrated simulation approach allowed for a more realistic evaluation of the CCUS facility’s performance under varying operational conditions. For example:

• Low CO₂ production naturally led to lower utilisation and venting in the CCUS system, reflecting reduced strain and capacity demand.
• High CO₂ production stresses the CCUS facility, increasing the likelihood of venting due to storage or utilisation constraints.

Benefits of a Monte Carlo-Based RAM Approach

This method of simulation, applying individual production profiles to each CCUS simulation lifecycle, represents a significant enhancement in modelling accuracy. It leverages the strength of Monte Carlo simulation, which produces a large pool of statistically significant data suitable for probabilistic analysis over traditional spreadsheet-based availability analyses. This enables a granular understanding of CO₂ venting probabilities, better risk profiling of system configurations, and data-driven decision-making around CCUS system design and operation. This approach provides a more robust and comprehensive assessment of the technology's potential, ultimately providing reliable data to the clients Net Zero portfolios.

By integrating RAM analysis between interconnected facilities (in this case, LNG and CCUS), and applying post-processing to predict venting on a per-simulation basis, a more accurate understanding of CO₂ capturing and venting behaviour over time is achieved. This advanced RAM methodology not only supports regulatory compliance and emissions reduction but also improves investment confidence and system resilience.