A concept for data-driven uncertainty quantification and its application to carbon dioxide storage in geological formations

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A hypothesis‐driven approach to optimize field campaigns (2012, background): “[9] Is a CO2 injection site safe [e.g., Oladyshkin et al., 2011a, 2011b]?”
Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations (2013, methodology): “In [57, 60], the authors of the current study pointed out that the efforts invested in improved physical conceptualization, numerical codes, and stochastic modeling can easily be overwhelmed by error through human subjectivity in data interpretation at a very early stage of modeling, and they propos”
Global sensitivity analysis: a flexible and efficient framework with an example from stochastic hydrogeology (2012, methodology): “According to Oladyshkin and Nowak [29,30], an orthogonal polynomial basis up to order d can be constructively defined for any arbitrary probability measure, given that xj has finite statistical moments (e.”