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Integration of distributed acoustic sensing for real-time seismic monitoring of a geothermal field

地温梯度 背景(考古学) 云计算 地热能 诱发地震 计算机科学 领域(数学) 实时计算 工程类 土木工程 地质学 地球物理学 古生物学 数学 纯数学 操作系统
作者
Jérôme Azzola,Katja Thiemann,Emmanuel Gaucher
出处
期刊:Geothermal Energy [Springer Science+Business Media]
卷期号:11 (1) 被引量:1
标识
DOI:10.1186/s40517-023-00272-4
摘要

Abstract To accelerate the energy transition, the exploitation of deep geothermal reservoirs is becoming a priority to supply district heating networks in areas with high potential for geothermal applications. However, the sustainable development of the resource exploitation implies minimizing the associated risks, in particular related to induced seismicity, while optimizing operational processes. Besides, the growth of this energy sector, often supported by financial aid programs, provides resources to the industry that were not available in the past to implement advanced monitoring strategies. In this context, we present a monitoring system establishing Distributed Acoustic Sensing (DAS) as an effective component of the seismic network used for the monitoring of the geothermal field of Schäftlarnstraße (Munich, Germany). We also investigate its potential for real-time seismic monitoring in an urban environment and for risk mitigation. The monitoring system is based on a data management system linking the on-site acquisition infrastructure, including the fiber optic cable deployed in an injection well and the associated DAS interrogator, to a cloud Internet-of-Things (IoT) platform. The latter is designed to deliver both a secure storage environment for the DAS recordings and optimized computing resources for their processing. The proposed solution has been tested over a six-month period under operating conditions of the geothermal field. The survey proves the feasibility of efficiently acquiring and processing the large flow of continuous DAS data. The processing outcomes, emphasized by two detected local seismic events, demonstrate the suitability of DAS, cemented behind the casing of a flowing well, for (micro-) seismic monitoring of the geothermal site. The processing applied to the data takes advantage of the high spatial density of the acquisitions for their de-noising and for the detection of events. We find that the DAS monitoring system is capable of successfully detecting an event that could not be detected by the standard surface or shallow-borehole 3C-seismometers, despite noisy conditions associated with the urban environment and the field operation. The six-month test period demonstrates the potential of DAS to be integrated as a routine seismic monitoring component of an operating geothermal field. In addition, it highlights its advantageous role as a complement to surface seismometer-based networks, particularly in urban environments.

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