Advancing Seismic Monitoring for Geothermal Energy with Distributed Acoustic Sensing
Jérôme Azzola
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences (AGW), Department of Geothermal Energy and Reservoir Technology
Seismic monitoring is a key component of geothermal energy projects, supporting both reservoir characterization and the management of induced seismicity. In recent years, Distributed Acoustic Sensing (DAS) has emerged as an attractive complement to conventional seismic instrumentation, giving access to dense spatial sampling and novel perspectives on wavefield observation. This seminar will present recent surveys in seismic monitoring using DAS, exploring various deployment configurations, including downhole installations in wells, dedicated surface arrays, and existing telecommunication fibers. We investigate the capabilities and limitations of DAS in these various geometries for detecting and characterizing local microseismic events. Results from self-deployed cables in wells or with 3D configurations highlight the potential of DAS-based monitoring to improve detection sensitivity and provide a comprehensive seismic monitoring workflow. Comparisons with conventional seismometer stations provide insight into differences in the instrument response and the reliability of results derived from DAS strain-rate. We then focus on the use of fibers from underutilized telecommunication infrastructures (dark fibers). This study is conducted in the framework of the DeepStor geothermal research infrastructure on the KIT Campus North, representative of an urban environment. We demonstrate that DAS on dark fibers can capture the propagation of low-magnitude local and regional seismic events along fiber paths, even under noisy conditions and without non-specialized installations. We show that the high spatial density and the wavefield spatial coherency can compensate for weak signal-to-noise ratios. Furthermore, we use anthropogenic noise sources such as public transport to apply Multichannel Analysis of Surface Waves (MASW) and analyze near-surface properties as well as their temporal evolution. Overall, these results highlight the potential of DAS on dark fibers as a scalable solution for seismic monitoring in geothermal applications and beyond.
