• Energy Research

AbstractHarvesting geothermal energy often leads to a pressure drop in reservoirs, decreasing their profitability and promoting the formation of steam caps. While steam caps are valuable energy resources, they also alter the reservoir thermodynamics. Accurately measuring the steam fraction in reservoirs is essential for both operational and economic perspectives. However, steam content estimations are very limited both in space and time since current methods rely on direct measurements within production wells. Besides, these estimations normally present large uncertainties. Here, we present a pioneering method for indirectly sampling the steam content in the subsurface using the ever-present seismic background noise. We observe a consistent annual velocity drop in the Hengill geothermal field (Iceland) and establish a correlation between the velocity drop and steam buildup using in-situ borehole data. This application opens new avenues to track the evolution of any gas reservoir in the crust with a surface-based and cost-effective method.

In recent years the analysis of the variations of seismic background signal recorded in temporal deployments of seismic stations near river channels has proved to be a useful tool to monitor river flow, even for modest discharges. The objective of this work is to apply seismic methods to the characterization of the snowmelt process in the Pyrenees, by developing an innovative approach based on the hierarchical classification of the daily spectrograms. The CANF seismic broad-band station, part of the Geodyn facility in the Laboratorio Subterráneo de Canfranc (LSC), is located in an underground tunnel in the Central Pyrenees, at about 400 m of the Aragón River channel, hence providing an excellent opportunity to explore the possibilities of the seismic monitoring of hydrological events at long term scale. We focus here on the identification and analysis of seismic signals generated by variations in river discharge due to snow melting during a period of six years (2011–2016). During snowmelt episodes, the temporal variations of the discharge at the drainage river result in seismic signals with specific characteristics allowing their discrimination from other sources of background vibrations. We have developed a methodology that use seismic data to monitor the time occurrence and properties of the thawing stages. The proposed method is based on the use of hierarchical clustering techniques to classify the daily seismic spectra according to their similarity. This allows us to discriminate up to four different types of episodes, evidencing changes in the duration and intensity of the melting process which in turn depends on variations in the meteorological and hydrological conditions. The analysis of six years of continuous seismic data from this innovative procedure shows that seismic data can be used to monitor snowmelt on long-term time scale and hence contribute to climate change studies. PLoS ONE, 14 (10) ISSN:1932-6203