Geology topics

Mark Willis

Publications and source records attributed to Mark Willis.

4 recordsLinked to original sources

USGS2014

Dispersion analysis of passive surface-wave noise generated during hydraulic-fracturing operations

Surface-wave dispersion analysis is useful for estimating near-surface shear-wave velocity models, designing receiver arrays, and suppressing surface waves. Here, we analyze whether passive seismic noise generated during hydraulic-fracturing operations can be used to extract surface-wave dispersion characteristics. Applying seismic interferometry to noise measurements, we extract surface waves by cross-correlating several minutes of passive records; this approach is distinct from previous studies that used hours or days of passive records for cross-correlation. For comparison, we also perform dispersion analysis for an active-source array that has some receivers in common with the passive array. The active and passive data show good agreement in the dispersive character of the fundamental-mode surface-waves. For the higher mode surface waves, however, active and passive data resolve the dispersive properties at different frequency ranges. To demonstrate an application of dispersion analysis, we invert the observed surface-wave dispersion characteristics to determine the near-surface, one-dimensional shear-wave velocity.

Journal of Applied Geophysics↗

USGS2013

An effective noise-suppression technique for surface microseismic data

The presence of strong surface-wave noise in surface microseismic data may decrease the utility of these data. We implement a technique, based on the distinct characteristics that microseismic signal and noise show in the τ‐p domain, to suppress surface-wave noise in microseismic data. Because most microseismic source mechanisms are deviatoric, preprocessing is necessary to correct for the nonuniform radiation pattern prior to transforming the data to the τ‐p domain. We employ a scanning approach, similar to semblance analysis, to test all possible double-couple orientations to determine an estimated orientation that best accounts for the polarity pattern of any microseismic events. We then correct the polarity of the data traces according to this pattern, prior to conducting signal-noise separation in the τ‐p domain. We apply our noise-suppression technique to two surface passive-seismic data sets from different acquisition surveys. The first data set includes a synthetic microseismic event added to field passive noise recorded by an areal receiver array distributed over a Barnett Formation reservoir undergoing hydraulic fracturing. The second data set is field microseismic data recorded by receivers arranged in a star-shaped array, over a Bakken Shale reservoir during a hydraulic-fracturing process. Our technique significantly improves the signal-to-noise ratios of the microseismic events and preserves the waveforms at the individual traces. We illustrate that the enhancement in signal-to-noise ratio also results in improved imaging of the microseismic hypocenter.

Geophysics↗

USGS2013

Noise suppression in surface microseismic data by τ-p transform

Surface passive seismic methods are receiving increased attention for monitoring changes in reservoirs during the production of unconventional oil and gas. However, in passive seismic data the strong cultural and ambient noise (mainly surface-waves) decreases the effectiveness of these techniques. Hence, suppression of surface-waves is a critical step in surface microseismic monitoring. We apply a noise suppression technique, based on the τ — p transform, to a surface passive seismic dataset recorded over a Barnett Shale reservoir undergoing a hydraulic fracturing process. This technique not only improves the signal-to-noise ratios of added synthetic microseismic events, but it also preserves the event waveforms.

Conference Paper↗

USGS2012

Noise suppression in surface microseismic data

We introduce a passive noise suppression technique, based on the τ − p transform. In the τ − p domain, one can separate microseismic events from surface noise based on distinct characteristics that are not visible in the time-offset domain. By applying the inverse τ − p transform to the separated microseismic event, we suppress the surface noise in the data. Our technique significantly improves the signal-to-noise ratios of the microseismic events and is superior to existing techniques for passive noise suppression in the sense that it preserves the waveform. We introduce a passive noise suppression technique, based on the τ − p transform. In the τ − p domain, one can separate microseismic events from surface noise based on distinct characteristics that are not visible in the time-offset domain. By applying the inverse τ − p transform to the separated microseismic event, we suppress the surface noise in the data. Our technique significantly improves the signal-to-noise ratios of the microseismic events and is superior to existing techniques for passive noise suppression in the sense that it preserves the waveform.

The Leading Edge↗