Impact of Uncertainty on Thermal and Chemical Tracers for EGS Characterization: A Framework for Thermo-Chemical Smart Tracers

摘要

A major issue to overcome when characterizing a deep fractured reservoir is that of data limitation due to accessibility and affordability. Geological characterization data include, but are not limited to, measurements of fracture density, orientation, extent, and aperture. All of which are taken at the field scale through a very sparse limited number of deep boreholes. These types of data are often reduced to probability distribution functions for predictive modeling and simulation in a stochastic discrete framework. Stochastic discrete fracture network (SDFN) models enable, through Monte Carlo simulations, the probabilistic assessment of flow and transport phenomena that are not adequately captured using continuum models. Despite the fundamental uncertainties inherited within the probabilistic reduction of the sparse data collected, very little work has been conducted on quantifying uncertainty on the reduced probabilistic distribution functions. Using nested Monte Carlo simulations, we investigated the impact of parameter uncertainties of the discrete fracture network on the flow, heat and mass transport using physical characteristics such as the hydraulic conductivity tensor, production temperatures and peak arrival time.