Geology topics

Jyoti Behura

Publications and source records attributed to Jyoti Behura.

5 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 automated cross-correlation based event detection technique and its application to surface passive data set

In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running-window energy ratio of the short-term average to the long-term average of the passive seismic data for each trace. We show that for the common case of a low signal-to-noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross-correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal-to-noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.

Geophysical Prospecting↗

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↗