Automated Full Waveform Detection and Location Algorithm of Acoustic Emissions from Hydraulic Fracturing Experiment. (2017)

• Record Type: Journal Article
• Authors: Comino, José Ángel López; Heimann, Sebastian; Cesca, Simone; Milkereit, Claus; Dahm, Torsten; Zang, Arno
• Is Part Of: Procedia engineering. Volume 191(2017)
• Page Start: 697
• Page End: 702
• Publication Date: 2017
• Subjects: Hydraulic fracturing -- induced seismicity -- acoustic emission -- full waveform detection -- detection performance; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.05.234
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: A near field network with 11 acoustic emission (AE) sensors was installed for the in situ underground experiment (Nova project 54-14-1) that took place 410 m below surface in the Äspö Hard Rock Laboratory, Sweden. The acquisition system for the piezo-electrical sensors has been improved to record signals with 1 MHz sampling rate, to detect signals produced by weaker sources and enhance the microseismic catalogue. The acquisition system was capable to operate in trigger and continuous mode. The basic idea of the experiment was to compare hydraulic fracturing growth and induced seismicity under controlled conditions for different loading scenarios as conventional versus progressive, and pulse-like water injections. In this work, we consider continuous recordings and apply recently developed automated full waveform detection and location algorithms which are based on the stacking of characteristic functions calculated from squared amplitudes. Waveform stacking and coherence techniques are adapted to detect and locate AE signals for massive datasets with extremely high sampling. We significantly increase the detection rate in comparison to trigger mode routines. Most detection concentrated during the fluid injection occurred around the fracking stages. Frequency-magnitude distribution characteristics are investigated using a relative magnitude scale estimated from the amplitude recorded at AE sensors. We demonstrate that the stacking of characteristic functions yields to a significant improvement of the detection and location also in presence of noisy records, supporting the adoption of similar techniques for other induced and natural seismic activity monitoring systems.