Abstract Numerical simulations are widely used as tools to aid in understanding processes or making predictions about states and their evolution in time. However, in the process of simulation setup, a multitude of choices and simplifications must be made—from the definition of the physical laws implemented, to model discretization and spatial parameterization, and the definition of initial and boundary conditions. In addition, the defined parameters are often subject to significant uncertainties. It therefore becomes paramount to investigate the extent to which a model output, such as the predicted temperature or pressure state at a location, is influenced by a certain input parameter, such as the thermal conductivity of a geological unit. Attempts to resolve this question are thus included in many research papers, and are generally referred to as sensitivity analyses. However, only a small portion of studies actually perform a sensitivity analysis in a mathematical sense. We propose that this omission occurs for two reasons: (1) a lack of knowledge about the different types of sensitivity analyses, and how they can be properly described and formulated, and (2) a lack of awareness about the tools that are readily available to perform structured sensitivity analyses. In this contribution, we aim to fill this gap through a review of the mathematical foundations of sensitivity analyses, a presentation of the different types, several intuitive examples, and a recommendation for the choice of a suitable method, with the aim of providing guidance for wider application of suitable sensitivity analyses in subsurface process simulations. Graphic Abstract
Degen et al. (Sat,) studied this question.