Dealing with uncertainty in risk assessments in early stages of a CO2 geological storage project: comparison of pure-probabilistic and fuzzy-probabilistic frameworks
Résumé
CO
2
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 uncer-
tainties: a classical probabilistic framework and a prob-
abilistic-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 rep-
resented 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 episte-
mic 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