Predicting thermal performance of an enhanced geothermal system from
tracer tests in a data assimilation framework
Abstract
Predicting the thermal performance of an enhanced geothermal system
(EGS) requires a comprehensive characterization of the underlying
fracture flow patterns from practically available data such as tracer
data. However, due to the inherent complexities of subsurface fractures
and the generally insufficient geological/geophysical data, interpreting
tracer data for fracture flow characterization and thermal prediction
remains a challenging task. The present study aims to tackle the
challenge by leveraging a data assimilation method to maximize the
utilization of information inherently contained in tracer data, and
meanwhile maintain the flexibility to handle various uncertainties. A
tracer data interpretation framework was proposed with the following
three components integrated: 1) We use principal component analysis
(PCA) to reduce the dimensionality of model parameter space. 2) We use
ES-MDA (ensemble smoother with multiple data assimilation) to invert for
fracture aperture/flow fields and obtain posterior model ensembles for
uncertainty quantification. Various data types are assimilated jointly
to improve the predictive ability of the posterior ensemble. 3) The
inverted fracture aperture fields are then incorporated into reservoir
models to predict thermal performance. We developed a field-scale EGS
model to verify the ability of the framework to characterize highly
heterogeneous fracture aperture/flow fields and predicting thermal
performance. We also applied the framework to a meso-scale field
experiment to demonstrate its potential application in real-world
geothermal reservoirs. The results indicate that the proposed framework
can effectively retrieve fracture flow information from tracer data for
thermal prediction and uncertainty quantification, and thus provide
informative guidance for EGS optimization and risk management.