Dealing with uncertainty in risk assessments in early stages of a CO2 geological storage project: comparison of pure-probabilistic and fuzzy-probabilistic frameworks

Abstract

CO2 capture and storage is recognized as a promising solution among others to tackle greenhouse gas emissions. This technology requires robust risk assessment and management from the early stages of the project (i.e. during the site selection phase, prior to injection), which is a challenging task due to the high level of aleatory and epistemic uncertainties. This paper aims at implementing and comparing two frameworks for dealing with uncertainties: a classical probabilistic framework and a probabilistic-fuzzy-based (i.e. jointly combining fuzzy sets and probabilities) one. The comparison of both frameworks is illustrated for assessing the risk related to leakage of brine through an abandoned well on a realistic site in the Paris basin (France). For brine leakage flow computation, a semi-analytical model, requiring 25 input parameters, is used. Depending on the framework, available data is represented in a different manner (either using classical probability laws or interval-valued tools). Though the fuzzy-probabilistic framework for uncertainty propagation is computationally more expensive, it presents the major advantage to highlight situations of high degree of epistemic uncertainty: this enables nuancing a too-optimistic decision-making only supported by a single probabilistic curve (i.e. using the Monte-Carlo results). On this basis, we demonstrate how fuzzy-based sensitivity analysis can help identifying how to reduce the imprecision in an effective way, which has useful applications for additional studies. This study highlights the importance of choices in the mathematical tools for representing the lack of knowledge especially in the early phases of the project, where data is scarce, incomplete and imprecise.