Practical notes on local data‐worth analysis

These notes discuss the usefulness, limitations, and potential pitfalls of using sensitivity indices as a means to evaluate data worth and to guide the formulation and solution of inverse problems. A sensitivity analysis examines changes in model output variables with respect to changes in model input parameters. It appears straightforward to use this information to select influential parameters that should be subjected to estimation by inverse modeling and to identify the observations that contain information about these parameters and thus may be useful as calibration points. However, the results of such a sensitivity analysis do not account for parameter correlations and redundancies in observations and may not properly separate between calibration and prediction targets if used as criteria that guide inverse modeling; they may thus yield misleading recommendations about parameter identifiability and data worth. These issues (and some remedies) are discussed using an illustrative example, in which we examine the value of data sets potentially used for the calibration of a geothermal reservoir model. These notes highlight the importance of carefully formulating the objectives of a simulation study, which controls the setup of the inverse problem and related data needs.