The EVOLVE workflow uses a distance-based generalized sensitivity analysis (dGSA) (Fenwick et al., 2014). The concept is straightforward. It compares the cumulative distribution function (CDF) of each parameter in each cluster to the CDF of the entire population (all clusters). If the differences are statistically significant, the parameter is deemed to be sensitive for the distance metric selected, otherwise it is insensitive. 

Advantages to Generalized Sensitivity Analysis:

• Any type of parameter, discrete or continuous, can be analyzed in this manner.  
• No response surfaces are created - the analysis is directly using the models transformed into metric space.  
• No requirement that the modeler assume a functional form of the response.  
• Spatial uncertainty (stochastic noise) is handled naturally in the GSA approach
• Multiple responses can be considered (such as well-by-well oil production and pressures over historical time period)

Use novel, intuitive sensitivity analysis using metric space methods.

• Visualize model uncertainty
• Intuitive, visual understanding of parameter impacts on model response

Novel procedure provides alternative, complimentary technique to traditional sensitivity analysis

• Relies on clustering analysis in metric space
• Completely general approach.
• Rigorous statistical measurement of sensitivity