Full-waveform-based characterization of acoustic emission activity in a mine-scale experiment: a comparison of conventional and advanced hydraulic fracturing schemes

Abstract

SUMMARY Understanding fracturing processes and the hydromechanical relation to induced seismicity is a key question for enhanced geothermal systems (EGS). Commonly massive fluid injection, predominately causing hydroshearing, are used in large-scale EGS but also hydraulic fracturing approaches were discussed. To evaluate the applicability of hydraulic fracturing techniques in EGS, six in situ, multistage hydraulic fracturing experiments with three different injection schemes were performed under controlled conditions in crystalline rock at the Äspö Hard Rock Laboratory (Sweden). During the experiments the near-field ground motion was continuously recorded by 11 piezoelectric borehole sensors with a sampling rate of 1 MHz. The sensor network covered a volume of 30×30×30 m around a horizontal, 28-m-long injection borehole at a depth of 410 m. To extract and characterize massive, induced, high-frequency acoustic emission (AE) activity from continuous recordings, a semi-automated workflow was developed relying on full waveform based detection, classification and location procedures. The approach extended the AE catalogue from 196 triggered events in previous studies to more than 19 600 located AEs. The enhanced catalogue, for the first time, allows a detailed analysis of induced seismicity during single hydraulic fracturing experiments, including the individual fracturing stages and the comparison between injection schemes. Beside the detailed study of the spatio-temporal patterns, event clusters and the growth of seismic clouds, we estimate relative magnitudes and b-values of AEs for conventional, cyclic progressive and dynamic pulse injection schemes, the latter two being fatigue hydraulic fracturing techniques. While the conventional fracturing leads to AE patterns clustered in planar regions, indicating the generation of a single main fracture plane, the cyclic progressive injection scheme results in a more diffuse, cloud-like AE distribution, indicating the activation of a more complex fracture network. For a given amount of hydraulic energy (pressure multiplied by injected volume) pumped into the system, the cyclic progressive scheme is characterized by a lower rate of seismicity, lower maximum magnitudes and significantly larger b-values, implying an increased number of small events relative to the large ones. To our knowledge, this is the first direct comparison of high resolution seismicity in a mine-scale experiment induced by different hydraulic fracturing schemes.

Relaxation damage control via fatigue-hydraulic fracturing in granitic rock as inferred from laboratory-, mine-, and field-scale experiments

“…AE was absent in both post-shut-in-and cyclicfatigue-fracturing stages only for AE events with magnitude estimation. Weaker events could also be found in the seismic catalogue in the post-shut-in and during cyclic injection 46 .…”

“…The plotted induced-seismicity parameters are the cumulative number and magnitude of the AE events (Fig. 2, black curve and red dots) obtained from the continuous-recording system 46 . The upper-two panels in Fig.…”

“…4b, HF3 fracture trace A and B). Niemz et al 46 mapped the hydraulic fractures farther away from the wellbore using the expectation-maximisation algorithm. A single plane or multiples planes were fitted to the cloud of the AE hypocentres by maximising the expectation value of the underlying Gaussian distributions.…”

“…Upon completing the fracturing and re-fracturing stages, the borehole wall was mapped with an impression packer. The shape of the fractures and their extensions were reconstructed via the impression-packer results and the AE-hypocentre-tracking results 46 . The fracture aperture was computed via the extension of the AE cloud and the measured hydraulic back-flow values.…”

Hydraulic Fracturing‐Induced Seismicity

“…Applications of these types of detection algorithms to HF‐induced seismicity are just beginning (Dokht et al, 2019), with significant potential for these algorithms to improve the detection of HF‐induced sequences. While we have focused on detection of events, we acknowledge that advancements in the earthquake location procedure have also been important for discerning cases of induced earthquakes (Cesca & Grigoli, 2015; Grigoli et al, 2017; Kwiatek et al, 2013; Li, et al, 2020; Niemz et al, 2020; Waldhauser & Ellsworth, 2000). As HF‐induced seismicity frequently demonstrates strong spatiotemporal correlations with well operations, improving the seismic catalog can significantly increase the confidence in determining whether an earthquake sequence is associated with well operations.…”