A Multi-stage inversion framework for dynamic fracture characterization
and long-term thermal performance prediction in an Enhanced Geothermal
System
Abstract
Fractures play important roles in fluid and heat flow during heat
extraction from an enhanced geothermal system (EGS). Quantifying the
associated uncertainties in fractures is critical for predicting
long-term thermal performance of EGSs. Considerable advancements have
been made regarding the inversion of fracture characteristics such as
aperture distribution. However, most previous studies assumed a constant
fracture aperture to simplify the inversion, while both experimental and
numerical results indicated significant variations in fracture aperture
due to complex thermo-hydro-mechanical (THM) coupled processes during
heat extraction. This study introduces a multi-stage inversion framework
that integrates the Ensemble Smoother with Multiple Data Assimilation
(ES-MDA) with a THM coupled model to capture the dynamic evolution of
fracture aperture. The framework executes multiple aperture inversions
at different times during EGS operation. In each inversion stage, we use
ES-MDA to invert for fracture aperture by assimilating tracer data, and
then perform THM modeling to analyze fracture aperture evolution under
coupled THM processes and predict thermal performance. We propose a
principle to assure a smooth transition between two consecutive
inversion stages, that the posterior aperture fields obtained in an
inversion stage are used as the prior aperture fields for the following
stage, and the temperature field simulated in the previous inversion
stage serves as the initial temperature field for the following stage.
Application of the framework to a synthetic field-scale EGS model
demonstrates its efficacy in capturing the dynamic evolution of fracture
aperture, resulting in more accurate thermal predictions compared with
previous inversion methods assuming constant fracture aperture.