Compaction of the Groningen gas reservoir investigated with train noise

Abstract

Induced seismicity in the Groningen gas field in the Netherlands has been related to reservoir compaction caused by gas pressure depletion. In situ measurement of compaction is therefore relevant for seismic hazard assessment. In this study, we investigated the potential of passively recorded deep borehole noise data to detect temporal variations in the Groningen reservoir. Train signals recorded by an array of 10 geophones at reservoir depth were selected from the continuous noise data for two 5-month deployments in 2015. Interferometry by deconvolution was applied to the high-frequency train signals that acted as stable, repetitive noise sources. Direct intergeophone P and S wave traveltimes were then used to construct the P- and S-wave velocity structure along the geophone array. The resulting models agree with independently obtained velocity profiles and have very small errors. Most intergeophone P wave traveltimes showed decreasing traveltimes per deployment period, suggestive of compaction. However, the retrieved traveltime changes are very small, up to tens of microseconds per deployment period, with uncertainties that are of similar size, about 10 microseconds. An unambiguous interpretation in terms of compaction is therefore not warranted, although the 10 μs error per 5-month period is probably smaller than can be achieved from active time-lapse seismic surveys that are commonly used to measure reservoir compaction. The direct P-wave amplitudes of the train-signal deconvolutions were investigated for additional imprints of compaction. Whereas the P-wave amplitudes consistently increased during the second deployment, suggestive of compaction, no such trend was observed for the first deployment, rendering the interpretation of compaction inconclusive. Our results therefore present hints, but no obvious effects of compaction in the Groningen reservoir. Yet, this study demonstrates that the approach of deconvolution interferometry applied to deep borehole data allows monitoring of small temporal changes in the subsurface for stable repetitive noise sources such as trains.