Abstract
Geomechanical risk assessment is a critical component of geological CO2 storage because storage security depends on uncertain subsurface rock mass properties controlling deformation, failure, injectivity, and caprock sealing performance. Fuzzy set theory provides an effective framework for handling such uncertainty; however, the development of fuzzy membership functions (FMFs) for key geomechanical and petrophysical parameters remains insufficiently standardized and often relies on subjective assumptions. This study develops application-oriented FMFs for six key parameters: cohesion, internal friction angle, Young’s modulus, Poisson’s ratio, porosity, and permeability. The FMFs were derived from a compiled dataset of databases, geomechanical reports, and published studies on CCS sites and analogous formations using a box-plot-based procedure to parameterize five linguistic classes—Very Low, Low, Medium, High, and Very High—based on distribution statistics. The resulting FMFs used shoulder trapezoidal functions for extreme classes and triangular functions for intermediate classes. Consistency checks against published classifications and lithology-specific datasets showed broad agreement with established geomechanical and petrophysical knowledge. The proposed FMFs provide a transparent and reproducible basis for mapping uncertain subsurface parameters into linguistic classes and can support fuzzy inference and synthetic evaluation models for CCS site screening, storage integrity assessment, and decision support under geological uncertainty.