EGUsphere

Copernicus Publications

Göttingen, Germany

10.5194/egusphere-2022-1417

Towards real-time seismic monitoring of a geothermal plant using Distributed Acoustic Sensing

Azzola

Jerome

¹ Thiemann

Katja

² Gaucher

Emmanuel

https://orcid.org/0000-0001-5870-4113

Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences (AGW), Karlsruhe, Germany

Stadtwerke München GmbH, Munich, Germany

Bundesministerium für Wirtschaft und Energie

03EE4008C

14 12 2022

2022 1 29

2022

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

This article is available from https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1417/

The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1417/egusphere-2022-1417.pdf

Distributed Acoustic Sensing (DAS) is an emerging technology for acquiring seismic data on virtual sensors densely distributed along an optical fiber. The broadband response of the sensors, associated with the possibility of deploying fiber optic cables in harsh conditions and the relatively moderate cost of this sensing element gives clear perspectives for DAS in geothermal wells to contribute to the monitoring operations of geothermal plants. However, the technical feasibility of managing the large flow of data generated by the DAS and the suitability of the strain-rate acquisitions to monitor locally induced seismicity was yet to be assessed. We propose a monitoring concept establishing DAS as an effective component of the seismic monitoring of the Schäftlarnstraße geothermal plant (Munich, Germany). The underlying data management system links the existing on-site infrastructure, including the fiber optic cable deployed in one of the site’s injection wells and the associated DAS recorder, to a cloud Internet-of-Things (IoT) platform designed to deliver both a secure storage environment for the DAS acquisitions and optimized computing resources for their processing. The proposed solution was tested over a period of six months and showed the feasibility of efficiently acquiring and processing the large flow of continuous DAS data. For seismic risk mitigation purposes, we additionally investigate the potential of the monitoring concept to tend towards real-time monitoring. The processing outcomes, focusing especially on two detected local seismic events, demonstrates the relevance of DAS from geothermal wells for the (micro)seismic monitoring of the geothermal site. Despite the noisy operational conditions, the applied processing workflow takes advantage of the sensors’ high spatial density for data denoising and event triggering and highlights that higher detection sensitivity than conventional seismometers can be achieved. From a different perspective, further analyses of the DAS records confirm the logging capabilities of the technology, especially regarding well completion integrity. The 6-months test period shows that permanent DAS can be integrated as a routine seismic monitoring component of geothermal plants and advantageously complement surface seismometer-based networks, especially in urban environments.