Global and local sensitivity analysis of heat transport in fractured rock using a modified LH-OAT method

Thermal conductive heating is an in-situ groundwater remediation technique that can be implemented in weathered or fractured rock. However, the characterization and identification of heat transfer in fractured rock is challenging because of complex hydrogeological and thermodynamic processes, particularly for irregular heat source configurations. As an effective practice, sensitivity analysis has been widely used to screen, rank, and quantify the influential factors in complex systems. In this study, a three-dimensional numerical model was applied to investigate, using global and local sensitivity analyses, the significance of six input factors that influence the heating of fractured rock. The factors include the radius and energy delivery strength of the heat source (which were used to study the scale effect and heating processes), the fracture aperture, fracture spacing, groundwater velocity, and the thermal conductivity of the rock matrix. A discrete Latin Hypercube-One at A Time (LH-OAT) approach is proposed and utilized as an experimental design and data analysis method for the discrete input factors that apply to this study. We also used Machine Learning techniques to enhance the robustness of simulation results with a significant reduction of computational cost. The results show that at all locations within the heating area, the radius of the source and energy delivery strength are the most influential input factors. The scale effect associated with the radius of the heat source is significant for the rock matrix temperature. The groundwater flow related factors jointly determine the temperature variation in the upstream areas. The contribution of the thermal conductivity of the rock matrix to heat dissipation is nearly isotropic in the heating area. A nonlinear trending of sensitivity indices is observed for all factors in the input space. Based on these results, the discrete LH-OAT approach has been demonstrated to efficiently identify influential factors within a discrete input value space. The use of a two-way OAT perturbation approach is suggested to avoid potential errors caused by the one-way perturbation method for parameter ranking.