Global Sensitivity Analysis for Carbon Storage Site Ranking and Selection
Authors: Ma Juntao , Tan Bolei , Li Bingtao , DeSouza Minann , Holloway Carrie , De Gennaro Vincenzo 
Published: February 2025

Abstract
The main objective of this study is to define a solution that enables global sensitivity analysis (GSA) during carbon storage site ranking and selection while considering correlations between connected site properties. The aim is to identify the key site properties that contribute most to the ranking results' variability and provide guidance for data acquisition in the subsequent evaluation and characterization phase to decrease uncertainty and risk in site ranking and selection.
When there is no correlation between site properties, the Sobol' indices method is employed for GSA; otherwise, the Kucherenko indices method is used. Both methods explore the entire input space and rely on variance decomposition, while the Kucherenko indices method supports correlations by conditionally sampling with a Gaussian copula. The generated tornado plots of the total indices and the firstorder indices depict the key site properties contributing most to ranking variability, as well as noninfluential factors. The confidence intervals calculated through bootstrap analysis indicate the uncertainties of the sensitivity indices themselves.
The solution was applied to field data from an existing carbon storage site selection project, which uses the analytic hierarchy process (AHP) method for criteria weighting and the technique for order of preference by similarity to ideal solution (TOPSIS) method for ranking nine sites against 31 selected criteria. GSA was performed with the Sobol' indices method first, then the Kucherenko indices method was applied after adding correlations (e.g., positive correlation between formation permeability and porosity). The results revealed that, unlike local sensitivity analysis, which is based on deviations around a few reference values, GSA clearly indicates the key site properties and non-influential factors. The confidence intervals in tornado plots express the uncertainty of the sensitivity analysis results themselves. The analysis was performed in iterations, with noninfluential site properties in the current iteration excluded from the next iteration, which can eliminate noise and simplify the model, increasing the interpretability of the analysis results as well as computational efficiency. The same site property for different sites has similar input uncertainty because of similar data sources, so we grouped individual site properties into property groups, which provided a more concise and intuitive representation of the key factors driving the carbon storage site rankings.
Integrating the Sobol' and Kucherenko indices methods enables us to perform GSA for carbon storage site ranking and selection, even when there are correlations between site properties. Generated tornado plots of the total indices and the first-order indices with confidence intervals from bootstrap analysis indicate the key site properties and noninfluential factors with uncertainty. Analyzing the sensitivities of site properties through groups reveals the key factors more concisely and intuitively.