Microseismic Event Location by Cross-correlation Migration of Surface Array Data for Permanent Reservoir Monitoring

Grandi, S.; Oates, S.

doi:10.3997/2214-4609.201400497

Cited by 10 publications

(11 citation statements)

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“…The semblance reaches its maximum value of 1 for identical signals on all traces and the minimum value of 0 for a zero sum of the samples. The last method, which is based on XCORR, is derived from the method proposed by Grandi and Oates (2009); however, in our approach we align seismograms first, using the EMO correction, and then we compute the XCORR among all pairs of traces in a short time interval of duration 2W + 1. The main advantage of the semblance is its ability to identify and suppress high stack values that result from high noise on only a few receivers, which is a common problem for surface monitoring.…”

Section: Stacking Methodsmentioning

confidence: 99%

“…Using cross-correlations (XCORR) between receivers for the detection and location of microseismic events was proposed by Grandi and Oates (2009), who computed XCORR for all pairs of traces before applying moveout corrections. Then, the XCORR traces are stacked for different moveouts, and the maximum stack value corresponds to the source location (for noise-free data).…”

Section: A P P E N D I X a : C R O S S -C O R R E L A T I O N T E C Hmentioning

confidence: 99%

“…In this study, we modified the method of Grandi and Oates (2009) and computed XCORR on the event moveout (EMO)-corrected traces. As a result, the correlation must be repeated for different EMO corrections; however, it allowed us to shorten the temporal windows and lags over which XCORR is computed.…”

Section: A P P E N D I X a : C R O S S -C O R R E L A T I O N T E C Hmentioning

confidence: 99%

“…The method presented by Anikiev et al (2006), originally in 2D, was extended by Zhebel, Gajewski, and Vanelle (2011), who took into consideration a 3D model and tested a more realistic source than previously assumed (i.e., a non-explosive source). Other methods use cross-correlation, which is a function of the similarity between two events (Eisner et al 2008) or the similarity of signals on nearby receivers (Grandi and Oates 2009), or semblance, which is a function of the coherency of the signal (Chambers et al 2010). Other methods use cross-correlation, which is a function of the similarity between two events (Eisner et al 2008) or the similarity of signals on nearby receivers (Grandi and Oates 2009), or semblance, which is a function of the coherency of the signal (Chambers et al 2010).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of migration‐based location and detection methods for microseismic events

Trojanowski

Eisner

2016

Geophysical Prospecting

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Microseismic monitoring in the oil and gas industry commonly uses migration‐based methods to locate very weak microseismic events. The objective of this study is to compare the most popular migration‐based methods on a synthetic dataset that simulates a strike‐slip source mechanism event with a low signal‐to‐noise ratio recorded by surface receivers (vertical components). The results show the significance of accounting for the known source mechanism in the event detection and location procedures. For detection and location without such a correction, the ability to detect weak events is reduced. We show both numerically and theoretically that neglecting the source mechanism by using only absolute values of the amplitudes reduces noise suppression during stacking and, consequently, limits the possibility to retrieve weak microseismic events. On the other hand, even a simple correction to the data polarization used with otherwise ineffective methods can significantly improve detections and locations. A simple stacking of the data with a polarization correction provided clear event detection and location, but even better results were obtained for those data combined with methods that are based on semblance and cross‐correlation.

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Section: Stacking Methodsmentioning

confidence: 99%

Section: A P P E N D I X a : C R O S S -C O R R E L A T I O N T E C Hmentioning

confidence: 99%

Section: A P P E N D I X a : C R O S S -C O R R E L A T I O N T E C Hmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of migration‐based location and detection methods for microseismic events

Trojanowski

Eisner

2016

Geophysical Prospecting

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“…Schuster et al (2004) pointed out that seismic interferometric imaging can also be used to locate seismic sources, and Grandi et al (2009) successfully applied it to surface microseismic monitoring of reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Weighted-elastic-wave interferometric imaging of microseismic source location

Chen

Wang

2015

Appl. Geophys.

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Knowledge of the locations of seismic sources is critical for microseismic monitoring. Time-window-based elastic wave interferometric imaging and weightedelastic-wave (WEW) interferometric imaging are proposed and used to locate modeled microseismic sources. The proposed method improves the precision and eliminates artifacts in location profi les. Numerical experiments based on a horizontally layered isotropic medium have shown that the method offers the following advantages: It can deal with low-SNR microseismic data with velocity perturbations as well as relatively sparse receivers and still maintain relatively high precision despite the errors in the velocity model. Furthermore, it is more efficient than conventional traveltime inversion methods because interferometric imaging does not require traveltime picking. Numerical results using a 2D fault model have also suggested that the weighted-elastic-wave interferometric imaging can locate multiple sources with higher location precision than the time-reverse imaging method.

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Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

Tan

Schwarz

et al. 2020

Reviews of Geophysics

132

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Source locations provide fundamental information on earthquakes and lay the foundation for seismic monitoring at all scales. Seismic source location as a classical inverse problem has experienced significant methodological progress during the past century. Unlike the conventional traveltime‐based location methods that mainly utilize kinematic information, a new category of waveform‐based methods, including partial waveform stacking, time reverse imaging, wavefront tomography, and full waveform inversion, adapted from migration or stacking techniques in exploration seismology has emerged. Waveform‐based methods have shown promising results in characterizing weak seismic events at multiple scales, especially for abundant microearthquakes induced by hydraulic fracturing in unconventional and geothermal reservoirs or foreshock and aftershock activity potentially preceding tectonic earthquakes. This review presents a comprehensive summary of the current status of waveform‐based location methods, through elaboration of the methodological principles, categorization, and connections, as well as illustration of the applications to natural and induced/triggered seismicity, ranging from laboratory acoustic emission to field hydraulic fracturing‐induced seismicity, regional tectonic, and volcanic earthquakes. Taking into account recent developments in instrumentation and the increasing availability of more powerful computational resources, we highlight recent accomplishments and prevailing challenges of different waveform‐based location methods and what they promise to offer in the near future.

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Microseismic Event Location by Cross-correlation Migration of Surface Array Data for Permanent Reservoir Monitoring

Cited by 10 publications

References 0 publications

Comparison of migration‐based location and detection methods for microseismic events

Comparison of migration‐based location and detection methods for microseismic events

Weighted-elastic-wave interferometric imaging of microseismic source location

Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

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