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Microseismic interferometry

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Abstract and Figures

The past decade witnessed rapid development of the theory of seismic interferometry followed by numerous applications of interferometric approaches in seismic exploration and exploitation. This body of work, partially collected in the "Seismic Interferometry" supplement of Geophysics (2006), SEG's 2008 reprint volume edited by Wapenaar et al., and the "Interferometry Applications" special section of The Leading Edge (2011), conclusively demonstrates that a stack of crosscorrelations of traces recorded by two receivers over sources appropriately distributed in a three-dimensional heterogeneous Earth can retrieve a signal that would be observed at one receiver if another acted as a source of seismic waves. This assertion is applicable to both active-source data (many instructive geometries of this kind are examined by Schuster, 2009) and passive records of ambient noise; examples of the latter, summarized by Snieder and Wapenaar (2010), range from ultrasonics (Weaver and Lobkis, 2001) to global seismology (Shapiro et al., 2005).
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Passive seismic and microseismic—Part 2
1478 The Leading Edge December 2012
SPECIAL SECTION: P a s s ive seismic and microseismic—Part 2
Microseismic interferometry
The past decade witnessed rapid development of the
theory of seismic interferometry followed by numerous
applications of interferometric approaches in seismic
exploration and exploitation. is body of work, partially
collected in the “Seismic Interferometry” supplement of
G (2006), SEG’s 2008 reprint volume edited by
Wapenaar et al., and the “Interferometry Applications” special
section of e Leading Edge (2011), conclusively demonstrates
that a stack of crosscorrelations of traces recorded by two
receivers over sources appropriately distributed in a three-
dimensional heterogeneous Earth can retrieve a signal that
would be observed at one receiver if another acted as a source
of seismic waves. is assertion is applicable to both active-
source data (many instructive geometries of this kind are
examined by Schuster, 2009) and passive records of ambient
noise; examples of the latter, summarized by Snieder and
Wapenaar (2010), range from ultrasonics (Weaver and
Lobkis, 2001) to global seismology (Shapiro et al., 2005).
Passive seismic interferometry, sometimes called stochas-
tic interferometry, is especially well suited for downhole mi-
croseismic applications. Indeed, microseismic monitoring of
hydraulic stimulations of low-permeability reservoirs typically
lasts for days, supplying abundant data for turning geophones
into virtual sources (in the terminology of Bakulin and Cal-
vert, 2004) and analyzing the obtained outputs. To the best
of our knowledge, this opportunity was first recognized by
Miyazawa et al. (2008), who reconstructed the body P- and
S-waves propagating along a wellbore from steam-injection
noise recorded by three-component (3C) receivers. Essential-
ly, we follow their footsteps but with two modifications that
VLADIMIR GRECHKA, Marathon Oil Company
YANG ZHAO, University of California, Berkeley
appear to be important in practice. First, we seem to be able
to retrieve meaningful, clean, and straightforwardly interpre-
table signals from crosscorrelations of minutes of passive data
as opposed to a month-long record employed by Miyazawa
et al. (2008). Second, we do not explicitly remove locatable
microseismic events from the crosscorrelation process and,
instead, let normalization and stacking smooth out the as-
sociated amplitude anomalies.
In what follows, we describe the results of interferometry
applied to three downhole microseismic data sets acquired
in the hydrocarbon-bearing Niobrara, Eagle Ford, and Bak-
ken formations. Although the available 3C data make it theo-
retically possible to recover full elastic Green’s tensors for any
receiver pair, we restrict the scope of this article to a single,
along-the-well component of our 3C records. Depending
on the survey geometry, crosscorrelations of this component
yield either P- or S-waves traveling between the receivers,
which allow us to estimate the associated velocities that can
be used to build models for microseismic data processing.
We begin with outlining our computational procedure that
was performed in the time, t, domain. Let Ws,r(t) be the nor-
malized, along-the-well component of 3C seismic trace that
belongs to the shot gather s and is recorded by the receiver r.
Our usage of the term “receiver” is conventional because we
know where receiver r is located and what it records; the term
“shot gather,” however, needs to be explained. Because pas-
sive data, such as those displayed in Figure 1, contain waves
arriving from unknown natural sources to our receivers in
a selected time interval T, we define a shot gather as data
recorded by the receiver string during that time interval. Ac-
cording to the adopted definition, Figure 1 shows 1-s long (T
= 1 s) shot gather.
We take two traces ws,r1(t) and ws,r2(t) of shot gather s re-
corded at r1 and r2 and crosscorrelate them. We then stack the
obtained crosscorrelations over a subset of the available shot
gathers. e spectrum of the result,
where o denotes the time lag, is proportional to the sum of
spectra of the causal and acausal band-limited Green’s func-
tions between r1 and r2 if the physical sources (for instance,
contributing to the gather in Figure 1) have random and
uncorrelated spatial locations and amplitudes (Weaver and
Lobkis, 2001).
A few operational comments are now in order.
1) We normalize traces ws,r prior to crosscorrelating them to
suppress contributions of strong bursts of energy (e.g., mi-
croseismic events) whose signal-to-noise ratios can reach
Figure 1. Randomly chosen 1-s long microseismic shot gather recorded
by a string of 10 downhole receivers. is gather represents typical
input data for the case studies discussed in the paper.
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December 2012 The Leading Edge 1479
Passive seismic and microseismic—Part 2
over 100. Although we find this type of normalization
convenient because it eliminates the need to establish a
threshold for ambient noise and filter the input data ac-
cordingly, such an equalization of amplitudes is not abso-
lutely critical. e reason for its unimportance appears to
be a significant violation of the assumption of randomness
of natural seismic sources (Weaver and Lobkis, 2006), the
violation that prevents one from retrieving the correct am-
plitudes of waves comprising W (Equation 1). If the true
amplitudes of waves are unrecoverable in our conditions
and obtaining the correct kinematics remains the only re-
alistically achievable goal, amplitudes of input traces can
be severely distorted without compromising the traveltime
information (see Figures 9 and 10).
2) e time interval T defining the length of a shot gather
is a free parameter that needs to be selected. After experi-
menting with intervals ranging from 0.5 to 15 s, we found
that a particular value of T has little influence on the final
result, that is, on traveltimes of the expected arrivals, as
long as T is several times greater than the length of the
maximum crosscorrelation time lag o.
3) We apply zero-phase band-pass filters to the produced
stacks W of crosscorrelations to enhance their visual ap-
pearance. e frequency bandwidths f of our filters, listed
in the figure captions, are sufficient to preserve useful in-
formation in each displayed interferometric output.
Zero-offset VSP
Our first data set was acquired in a vertical well drilled to
monitor hydraulic stimulation of the Niobrara Formation
(Colorado, USA). In this geometry, retrieving VSP (vertical
seismic profiling) data from microseismic noise is possible.
An example of such zero-offset VSP is presented in Figure
2a, in which only 1 minute of raw record, whose portion
is displayed in Figure 1, sufficed to produce a meaningful
output. As Figure 2a demonstrates, turning the shallowest
receiver into a source recovers a causal downgoing wave. Be-
cause we crosscorrelate the vertical along-the-well receiver
components, it ought to be a P-wave. Its nature as the body
P-wave propagating in rocks surrounding the borehole be-
comes clear once we compare its traveltimes with those (red
dots in Figure 2a) derived from a sonic log (Figure 2b) in a
nearby vertical well. Sources of noise generating the P-wave
in Figure 2a have to be located above the receivers and likely
relate to human activity on the well pad.
Let us note that another, arbitrarily chosen minute of data
would not yield exactly the output shown in Figure 2a be-
cause natural sources are distributed nonuniformly in space
Figure 2. (a) Zero-offset VSP retrieved from 1 minute of microseismic data ( T= 15 s, f = [10, 50] Hz) recorded above the Niobrara Formation.
e red dots in (a) indicate the times obtained by integrating the P-wave sonic log in (b).
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Passive seismic and microseismic—Part 2
Figure 3. Well trajectory (gray) and locations of receivers (triangles).
e dip of the lateral section covered by the receivers is 3°. e arrow
points in the direction of propagation of the P-wave reconstructed in
Figure 4.
Figure 4. Wave retrieved from crosscorrelations of 16 minutes of data
( T = 15 s, f = [20, 150] Hz) that propagates in the direction of the
arrow along the receiver string shown in Figure 3. e slope of the
dashed straight line implies the average apparent velocity of 4.5 km/s.
and nonperiodic in time. eir spatial nonuniformity is evi-
denced by the absence of upgoing waves in Figure 2a and the
lack of their temporal periodicity—by our inability to find
two sets of different shot gathers resulting in the same stack.
Still, all examined stacks exhibit P-waves similar to the one
in Figure 2a and make the differences in their propagation
times a useful measure of the uncertainty in reconstructing
kinematics of VSP-type data from microseismic noise.
Direct measurement of the horizontal velocity
Our success in recovering the VSP in Figure 2a suggests the
possibility of repeating the same process for receivers placed
in a horizontal well. is sort of interferometric reconstruc-
tion was attempted with microseismic data acquired in the
Eagle Ford (Texas, USA) by an array of eight receivers lo-
cated in an approximately horizontal section of a deviated
borehole (Figure 3). A stack of crosscorrelations of 16 min-
utes of data retrieves an arrival displayed in Figure 4. is
is an acausal wave propagating along the well from its toe
toward its heel as indicated by the arrow in Figure 3. To ex-
cite this wave, noise sources should be in or around the Eagle
Ford Formation southeast from the receivers. e existence
of such sources seems plausible because our receivers moni-
tored hydraulic treatments of a well located southeast from
the receivers and recorded several hundreds of locatable mi-
croseismic events (not shown).
Approximating the moveout of the dominant phase in
Figure 4 with a straight line yields the average apparent veloc-
ity of 4.5 km/s. is approximately horizontal velocity is to be
compared with the sonic log in Figure 5 that exhibits the ver-
tical velocity of about 4.1 km/s in the depth interval covered
by the receivers. If we disregard the difference in frequencies
of microseismic and sonic data and attribute the difference
in the vertical and horizontal velocities to anisotropy under
the assumption that a vertically transversely isotropic (VTI)
model is appropriate for the Eagle Ford shales, our two ve-
locities result in a omsen (1986) anisotropy coefficient ¡ of
approximately 0.1. While¡is notoriously difficult to measure
from the P-wave surface reflection data, here we estimated it
from passively recorded microseismic at no additional data-
acquisition cost. Clearly, the recovered value of ¡ can be used
in a velocity model constructed to locate the microseismicity.
Shear-wave crosswell survey
Our third example comes from a dual-well microseismic
survey (Figure 6) conducted in the Bakken (North Dakota,
USA). When we crosscorrelate the vertical components of
receivers in well 1 with those in well 2 and stack the crosscor-
relations, we expect to retrieve the shear (SV) waves propa-
gating from one well to another. Turning receivers in well 2
into sources yields the stacks of crosscorrelations presented
in Figure 7. To create the causal arrivals observed in Figure
7, natural sources have to be located to the west from well 2
(Figure 6). is time, we lack a hypothesis explaining their
physical origin because hydraulically stimulated wells moni-
tored from wells 1 and 2 were located to the east from well
1 (not shown).
e absence of knowledge of the sources of natural noise
does not prevent us from picking the traveltimes of the main
phase on seismograms in Figure 7 (red dots) and on seismo-
grams of other receivers in well 2 that also were turned into
sources. ose time picks yield a traveltime crossplot pre-
sented in Figure 8a. is crossplot, exhibiting a sharp time
increase and, hence, a pronounced velocity reduction at a
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December 2012 The Leading Edge 1481
Passive seismic and microseismic—Part 2
Figure 6. Geometry of dual-well microseismic monitoring in the
Bakken. e locations of receivers in two nearly vertical wells are
marked with the triangles.
Figure 5. Sonic log in a vertical well indicating the P-wave velocity of
about 4.1 km/s in the depth interval covered by the receivers (triangles)
in Figure 3.
Figure 7. Stacks of crosscorrelations of the vertical components of
receivers at depths 3102, 3200, 3245, and 3281 m in well 2 with the
vertical components of receivers in well 1 ( T = 0.5 s, f =[10, 100]
Hz). e duration of the observation is 300 × 0.5 s = 150 s. e red
dots are the automatic time picks of the main retrieved phase.
depth of about 3200 m in well 1, could constitute an input
to the crosswell traveltime tomography. Instead of perform-
ing a full-scale tomographic inversion, we extract the zero-
offset times (that is, the times between receivers in wells 1 and
2 that have the same depths) marked with the white line in
Figure 8a and, knowing the distances between the receivers in
the two wells, convert the obtained times into the average ve-
locities. e resulting depth velocity profile is shown with the
thick blocky line in Figure 8b. Overall, it matches the shear-
wave sonic log (thin line), proving that interferometry has
extracted the shear-wave crosswell data from natural noise.
e greatest velocity discrepancy between the sonic and
the reconstructed velocity profile, observed for the upper and
lower Bakken shales (Figure 8b), relates to the resolution of
the retrieved crosswell data. As seismograms in Figure 7 indi-
cate, the spectra of the crosswell data peak at approximately
40 Hz, yielding the dominant shear wavelength of about
75 m. At such a wavelength, the Bakken shale layers, whose
thicknesses are 8 and 15 m (Figure 8b), cannot be resolved
individually and contribute only to a smooth velocity decline
exhibited by the recovered blocky profile.
Concluding remarks and remaining issues
Interferometric processing of three passive data sets dis-
cussed in the paper demonstrates that noise, which would be
usually discarded, contains information useful for velocity-
model building. is information comes in the ability to
retrieve various crosswell and VSP-type data. Importantly,
based on our experience, natural sources of noise appear to
possess wide spatial apertures, allowing the successful recon-
struction of waves that travel directly between the downhole
receivers. While we can only speculate on the physical rea-
sons for those wide apertures, multiple scattering in the het-
erogeneous subsurface is likely to be one of them.
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1482 The Leading Edge December 2012
Passive seismic and microseismic—Part 2
Figure 8. (a) Traveltime picks of the dominant phases, such as those
in Figure 7, and (b) shear-wave sonic log (thin line) along with the
reconstructed S-wave vertical velocity (thick blocky line). e white
line in (a) indicates traveltimes extracted to calculate the blocky
velocity profile in (b). e triangles in (a) mark the locations of
receivers in wells 1 and 2 (Figure 6).
Figure 9. Sign of data displayed in Figure 1.
Figure 10. Comparison of traces given by Equations 1 and 2.
e black traces, computed with Equation 1, are identical to those
displayed in Figure 2a. e red traces were computed with Equation 2
for the same input data.
We expect the number of applications of interferometry in
microseismic to grow once practitioners recognize its value and
begin using the method. For instance, in addition to the pre-
sented crosswell example, the P- and SH-wave crosswell data,
similar to the SV-waves displayed in Figure 7, could be recov-
ered from crosscorrelations of the horizontal components of
the original records, producing a full 3C crosswell data set that
can be processed using the available tomographic techniques.
Another area of potential applications opens up if receivers are
left in place, enabling one to collect and analyze time-lapse
data without the need for employing active seismic sources.
Although many different applications can be envisaged,
they all will have to deal with the challenging issue of nonran-
domness of natural sources that entails the apparent inability
to extract true amplitudes from interferometric reconstruc-
tions. However, if we embrace this limitation as reality and
aim at obtaining the correct kinematics, we get a surprisingly
robust methodology that depends on the wave amplitudes
only weakly. To illustrate this point, we follow the idea of
Larose et al. (2004), who suggested that removing the ampli-
tude information from input data by replacing the traces with
their signs (Figure 9) or Equation 1 with
is not harmful for the traveltimes. Figure 10 shows that this
is, indeed, the case and computations performed with Equa-
tions 1 and 2 can be deemed identical for practical purposes.
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December 2012 The Leading Edge 1483
Passive seismic and microseismic—Part 2
e results obtained so far leave us with a straightforward
agenda: understand what microseismic interferometry can
provide, try to recover it, and learn from the experience.
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Acknowledgments: We are grateful to our Marathon colleagues Don
Caldwell, Shihong Chi, Yulia Khadeeva, Michael Pelissier, Jeffrey
Rutledge, and Lev Vernik for encouragement and helping with
data-handling issues. We thank Marathon Oil Company for the
permission to publish the article and guest editor Julie Shemeta for
useful editorial suggestions.
Corresponding author:
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... For the shallow downhole microseismic monitoring (< 700 m depth), Miyazawa et al. (2008) showed that month-long noise recordings may be used to reconstruct downgoing direct P-waves, using the vertical component of the record, and S-waves using the horizontal components of the record. For a deeper acquisition (≈ 3000 m) the vertical component may be used to reconstruct direct P-wave (Grechka and Zhao, 2012), which was achieved for several datasets and much shorter total record time (5 minutes). Vaezi and Van der Baan (2015) showed that tube wave could also be retrieved with seismic interferometry, and it was suggested that their dominance in a wide frequency range reveals tool-clamping problems. ...
... Thus band-pass filtering was necessary for identifying the body-wave emergence frequency as it was masked by the tube waves. We retrieve visible tube wave only for the datasets A and C, and we do not see it for the datasets B and D. In other works tube wave is also not always retrieved in the virtual-source gathers (Grechka and Zhao, 2012). Note that band-pass filtering is not so crucial in the case when there is only body wave in the virtual-source gathers. ...
For downhole microseismic monitoring of hydraulic fracturing, the acquisition is performed using a set of three-component (3C) seismic receivers attached firmly to the borehole wall by a clamping mechanism. Such an acquisition cannot be repeated, and it is focused on recording weak signals. Thus, proper installation of the receivers is especially crucial for microseismic applications. We have developed a case study of using a seismic-interferometry approach for assessing the receiver's installation quality from ambient-noise records. Crosscorrelation of one vertical receiver noise records with the others allows us to retrieve the direct body wave propagating along the receiver array. Our observations indicate that the inability to retrieve the direct body wave is an indicator of clamping issues. Our case study does not support the emergence-frequency hypothesis reported in the literature (that higher frequencies present in the retrieved body-wave spectrum imply better clamping quality). Another conclusion is that seismic-interferometry processing provides a stable assessment of the clamping quality only for the vertical receivers. Thus, one gets only partial diagnostics of the clamping quality for the 3C downhole tool. This is important because the horizontal components may be affected more by the clamping issues compared with the vertical components. The overall conclusion is that seismic-interferometry processing of noise records is recommended for the assessment of the downhole receiver installation prior to microseismic monitoring. It does not provide complete diagnostics but comes for free (does not need any additional technological operations or extra time).
... Miyazawa et al. (2008) extracted direct downgoing P and S waves up to 370 m depth in the frequency band 10-55 Hz from industrial noise at the surface. Grechka and Zhao (2012) summarized several applications of downhole seismic interferometry. They obtained a downgoing P wave with frequencies up to 50 Hz at nearly 2 km depth and also found horizontally propagating S waves between two boreholes. ...
... This indicates that the fluctuations are due to variations in anthropogenic noise. Previous studies by Grechka and Zhao (2012), Vaezi and Van der Baan (2015) and Behm (2016) have shown that surface activity can indeed be the dominant noise source up to 2 km depth. Our results confirm their observation, extending it to a depth of 3 km. ...
Full-text available
Noise interferometry has proven to be a powerful tool to image seismic structure. In this study we used data from 10 geophones located in a borehole at ∼3 km depth within the Groningen gas reservoir in the Netherlands. The continuous data cross-correlations show that noise predominantly comes in from above. The observed daily and weekly variations further indicate that the noise has an anthropogenic origin. The direct P wave emerges from the stacked vertical component cross-correlations with frequencies up to 80 Hz and the direct S wave is retrieved from the horizontal components with frequencies up to 50 Hz. The measured inter-geophone travel times were used to retrieve the P- and S-velocity structure along the borehole and a good agreement was found with well log data. In addition, from the S-wave polarizations, we determined azimuthal anisotropy with a fast direction of N65∘W±18∘ and an estimated magnitude of (4±2)%. The fast polarization direction corresponds to the present direction of maximum horizontal stress measured at nearby boreholes, but is also similar to the estimated paleostress direction.
... sources with higher frequency components than crustal-scale sources) have been studied (Nakata et al. 2011;Yuan et al. 2021;Zhang et al. 2021a). These ambient noise sources with higher frequencies were successfully applied to engineering-scale targets such as active fault imaging and bedrock detection (Grechka & Zhao 2012;Edme & Halliday 2016;Issa et al. 2017;Brenguier et al. 2019;Elita Li et al. 2019;Taylor et al. 2019;Zhang et al. 2019;Nilot et al. 2020;Polychronopoulou et al. 2020;Qian & Liu 2020;Chamarczuk et al. 2021;Zhang et al. 2021b;Draganov et al. 2009). ...
The imaging of subsurface structures is an essential task in subsurface engineering projects; it provides information regarding the locations of active faults and layer boundaries. Among the methods available for imaging of subsurface structures, the body wave imaging method using urban traffic noise has recently attracted attention because it permits continuous measurement at low cost in urban areas. However, because the urban traffic noise signal used for imaging on the engineering scale has characteristics that differ from the ambient noise used on the crustal scale, the conventional crustal-scale data processing workflow should be modified through systematic data analysis. In this study, traffic noise sources were systematically analyzed using field data obtained over the Xiadian fault in Hebei province, China. The traffic noise signals were recorded in various patterns because of diverse incoming directions and show marked amplitude changes depending on time of recording. The overlapping signals originating from opposite directions generate spurious events and noise in the seismic interferometry images; constant processing parameters cannot respond to the large amplitude changes. In this study, to remove surface waves with markedly changing amplitude, we applied actively varying threshold values to each set of traces using the moving average of amplitude changes within the trace. In addition, the signals originating from diverse directions were separated into negative and positive slopes through the f-k filter; the interference generated by overlapping signals was minimized by applying data processing (e.g. median filtering and high amplitude removal) separately to the negative and positive slopes of each simultaneously acquired trace gather. Due to the modified data processing workflow, most spurious events were successfully suppressed in the final stacked image compared with those produced using the conventional data processing workflow, and reflections were imaged more clearly. Fault spatial locations and layer boundary depth variation in the final image obtained by the modified processing workflow were similar to those reported in previous studies.
... Active body-wave reflection interferometry has been used extensively in oil and gas exploration (Bolshakov et al. 2011;Grechka et al. 2012). In comparison, application of passive body-wave reflection SI to subsurface imaging has been less successful because direct, refracted, and diffracted waves at the surface tend to be much stronger than the reflected body waves, which have fewer stationary source positions than the former waves. ...
Full-text available
To understand steeply dipping events in seismic reflection interferometry (SRI), we derived an expression that describes the difference in travel time (Δτ) from a diffractor to two receivers in two dimensions. For a fixed receiver interval, the expression shows that Δτ is zero when the diffractor is at the midpoint of the paired receivers, increases with an apparent velocity of half the medium velocity as the diffractor moves toward either receiver, and remains constant for a diffractor located on the same side of both receivers. The horizontal portion of Δτ is slightly skewed during the normal moveout correction, yielding a maximum peak of the horizontally stacked trace at a slightly smaller time than Δτ. Accordingly, the diffracted waves have an apparent velocity slightly higher than half of the medium velocity in a horizontally stacked image. This conformed to virtual data for an elastic two-layer model with a vertical boundary. We then generalized the expression to three dimensions, in which listric travel time curves were predicted for an oblique edge diffractor, a vertex diffractor offline from the receiver pair, or a buried diffractor. Based on both two- and three-dimensional analyses of the edge diffractor, we tentatively interpreted the linear and listric dipping events observed in the passive SRI image across the Korean Peninsula to have been caused by diffractors near the intersection of the profile and geologic boundaries.
... For deep borehole data, the assumption can be satisfied for noise distributions mainly above or below the vertically aligned sensors. Previous studies have shown that body waves along borehole can be extracted by stacking noise cross-correlations over certain time (Miyazawa et al. 2008;Grechka and Zhao 2012;Vaezi and Van der Baan 2015;Behm 2016). Here we calculated the normalized crosscorrelation C sr (t) (Richter et al. 2014;Durand et al. 2011): ...
Conference Paper
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The Groningen gas field in the Netherlands is the world's 7th largest onshore gas field and has been producing from 1963. Since 2013, the reservoir has been monitored by two geophone strings at reservoir level (3 km). For borehole SDM, 10 geophones with a natural frequency of 15-Hz are positioned from the top to bottom of the reservoir. We used seismic interferometry to determine, as accurately as possible, the inter-geophone P- and S-wave velocities from ambient noise. Cross-correlations were stacked for every 1 hour and 24(hours)*33(days) segments were obtained for each station pair. The cross-correlations show both diurnal and weekly variations reflecting fluctuations in cultural noise. The apparent P-wave travel time for each geophone pair is measured from the maximum of the vertical component cross-correlation for each of the hourly stacks. We used Kernel density estimations to obtain the maximum likelihood travel times of all the geophone pairs which were subsequently used to determine inter-geophone P-wave velocities. A good agreement was found between our estimated P velocity structure and well logging data. The S-velocity structure was obtained from the east-component cross-correlations. Because of the interference with P wave in east-component, the inferred S-velocity structure is less accurate.
... However, for microseismic data time alignment of the traces, which is the prerequisite of stacking, is generally unknown. Thus, researchers have developed algorithms based on cross-correlation to find the relative time delays between traces (Al-Shuhail, Kaka and Jervis 2013; Grechka and Zhao 2012). In contrast to the case of an active seismic where the source generates a controllable active wavelet, the wavelet of a microseismic event is unknown, although some empirical knowledge of the frequency domain characteristics may be available. ...
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Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose an autocorrelation-based stacking method that designs a denoising filter from all the traces, as well as a multi-channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace's autocorrelation is centered at zero in the lag domain. The effect of white noise is concentrated near zero lag, so the filter design requires a predictable adjustment of the zero-lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with colored noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.
... However, acquiring these images using conventional active sources (e.g., explosives, vibroseis, and airgun) is not always easily achievable because of costs, environmental concerns, and other practical issues. Therefore , the use of passive seismic methods, such as microseism reflection imaging (e.g., Toksöz, 1964; Tamakawa et al., 2010; Reshetnikov et al., 2012) and seismic interferometry with ambient noise (e.g., Draganov et al., 2009; Ruigrok et al., 2011, 2012; Grechka and Zhao, 2012), has recently become more attractive. In this sense, despite the fact that earthquakes are usually seen as abhorrent phenomena in human communities (due to the risks they pose), it is natural to also view weaker earthquakes as attractive natural resources if one can use their information in appropriate analyses. ...
We have developed a new imaging technique of subsurface heterogeneities that uses Sp-waves from natural earthquakes. This technique can be used as a first screening tool in frontier exploration areas before conventional active exploration. Analyzing Sp-waves from 28 earthquakes (M(j)2.0 to 4.2) recorded by two permanent seismic stations, we built an image of the distributions of velocity discontinuities in southeastern offshore Hokkaido, Japan, where intraplate earthquakes in the Pacific plate frequently occur. Our results indicated the presence of three horizontally continuous, distinct discontinuities corresponding to geologic boundaries estimated in a previous study. We also derived the frequency-dependent quality factor Q for P-and S-waves and use it as a method of characterizing physical properties of subsurface structure. The waveform traces with coherent Sp-phases in the southern part of the study area generally show a constant Q(S)/Q(P) ratio, and the waveform traces with randomly distributed phases in the northern part show a large variation of the Q(S)/Q(P) ratio (including several high values).
Borehole arrays are often preferred over surface installations for hydraulic-fracture monitoring of deep experiments due to their proximity to the treatment zone. Borehole geophone strings are typically clamped to the observation wellbore wall using electromechanical or magnetic devices in order for them to be in close contact with the surrounding formations and record the background noise and propagating wavefields related to the microseismic experiments. This contact needs to be maintained throughout the recording time. We have used seismic interferometry to assess the clamping quality of borehole geophone arrays. We determined that the characteristics of the retrieved crosscorrelation functions between a reference receiver and other receivers in an array are indicative of the clamping quality of the former geophone to the borehole wall. We have also defined the concept of separation frequency or emergence frequency as the frequency below which direct body waves propagating along the receiver line are clearly observed on the crosscorrelation gathers. The crosscorrelation gathers associated with poorly clamped geophones show predominantly tube waves or incoherent waveforms. Body waves only emerge below very low separation frequencies. The crosscorrelation gathers of relatively better coupled geophones, on the other hand, have higher separation frequencies. We have applied this method to four different borehole microseismic data sets, labeled here as A, B, C, and D, of which data set D was previously known to suffer from some clamping issues. Data sets B and C with inferred better coupling had separation frequencies of approximately 60 Hz, whereas the other two data sets are characterized by lower separation frequencies, 15 Hz for data set A and 20 Hz for data set D, suggesting relatively poorer coupling.
In hydraulic fracturing treatments, locating not only hydraulic fractures but also any pre-existing natural fractures and faults in a subsurface reservoir is very important. Hydraulic fractures can be tracked by locating microseismic events, but to identify the locations of natural fractures, an additional technique is required. In this paper, we present a method to image pre-existing fractures and faults near a borehole with virtual reverse vertical seismic profiling data or virtual single-well profiling data (limited to seismic reflection data) created from microseismic monitoring using seismic interferometry. The virtual source data contain reflections from natural fractures and faults, and these features can be imaged by applying migration to the virtual source data. However, the imaging zone of fractures in the proposed method is strongly dependent on the geographic extent of the microseismic events and the location and direction of the fracture. To verify our method, we produced virtual reverse vertical seismic profiling and single-well profiling data from synthetic microseismic data and compared them with data from real sources in the same relative position as the virtual sources. The results show that the reflection travel times from the fractures in the virtual source data agree well with travel times in the real-source data. By applying pre-stack depth migration to the virtual source data, images of the natural fractures were obtained with accurate locations. However, the migrated section of the single-well profiling data with both real and virtual sources contained spurious fracture images on the opposite side of the borehole. In the case of virtual single-well profiling data, we could produce correct migration images of fractures by adopting directional redatuming for which the occurrence region of microseismic events is divided into several subdivisions, and fractures located only on the opposite side of the borehole are imaged for each subdivision.
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We extract downward-propagating P-and S-waves from industrial noise generated by human and/or machine activity at the surface propagating down a borehole at Cold Lake, Al-berta, Canada, and measure shear-wave splitting from these data. The continuous seismic data are recorded at eight sen-sors along a downhole well during steam injection into a 420–470-m-deep oil reservoir. We crosscorrelate the wave-forms observed at the top sensor and other sensors to extract estimates of the direct P-and S-wave components of the Green's function that account for wave propagation between sensors. Fast high-frequency and slow low-frequency signals propagating vertically from the surface to the bottom are found for the vertical and horizontal components of the wave motion, which are identified with P-and S-waves, respective-ly. The fastest S-wave polarized in the east-northeast–west-southwest direction is about 1.9% faster than the slowest S-wave polarized in the northwest-southeast direction. The direction of polarization of the fast S-wave is rotated clock-wise by 40° from the maximum principal stress axis as esti-mated from the regional stress field. This study demonstrates the useful application of seismic interferometry to field data to determine structural parameters, which are P-and S-wave velocities and a shear-wave-splitting coefficient, with high accuracy.
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We present an imaging technique based on correlations of a multiply scattered wave field. Usually the Green's function h AB between two points (A,B) is determined by direct transmit/receive measurement. When this is impossible, one can exploit an other idea: if A and B are both passive sensors, h AB can be retrieved from the cross correlation of the fields received in A and B, the wave field being generated either by deterministic sources or by random noise. The validity of the technique is supported by a physical argument based on time-reversal invariance. Though the principle is applicable to all kinds of waves, it is illustrated here by experiments performed with ultrasound in the MHz range. A short ultrasonic pulse, sent through a highly scattering slab, generates a randomly scattered field. Behind the slab is the medium to image: it consists of four liquid layers with different sound speeds. The cross correlation of the field received on passive sensors located within the medium is used to estimate the speed of sound. The experimental results show that the sound-speed profile of the layered medium can be precisely imaged. We emphasize the role of wideband multiple scattering and of source averaging in the efficiency of the method, as well as the benefit of performing one-bit correlations. Applications to seismology are discussed.
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Most bulk elastic media are weakly anisotropic. The equations governing weak anisotropy are much simpler than those governing strong anisotropy, and they are much easier to grasp intuitively. These equations indicate that a certain anisotropic parameter (denoted delta ) controls most anisotropic phenomena of importance in exploration geophysics, some of which are nonnegligible even when the anisotropy is weak. The critical parameter alpha is an awkward combination of elastic parameters, a combination which is totally independent of horizontal velocity and which may be either positive or negative in natural contexts.-Author
If some ten years ago, one approached a highly skilled geophysicist randomly picked from our community and asked "How can you turn signals recorded at two receivers into what would be recorded if one acts as a source?", or "How can we use random noise from unknown sources for imaging?", or "How do we turn an earthquake into a buried virtual receiver?", the answer most likely would lie somewhere between "Uh, I don't know," to "What?! You can't do that!" However, in recent years these questions as well as others (possibly even stranger) have been answered due to a boom of creative scientific work in interferometry.
Seismic interferometry is an exciting new field in geophysics utilizing multiple scattering events to provide unprecedented views of the Earth's subsurface. This is the first book to describe the theory and practice of seismic interferometry with an emphasis on applications in exploration seismology. Exercises are provided at the end of each chapter, and the text is supplemented by online MATLAB codes that illustrate important ideas and allow readers to generate synthetic traces and invert these to determine the Earth's reflectivity structure. Later chapters reinforce these principles by deriving the rigorous mathematics of seismic interferometry. Incorporating examples that apply interferometric imaging to synthetic and field data, from applied geophysics and earthquake seismology, this book is a valuable reference for academic researchers and oil industry professionals. It can also be used to teach a one-semester course for advanced students in geophysics and petroleum engineering.
Recent developments in seismology, ultrasonics, and underwater acoustics have led to a radical change in the way scientists think about ambient noise - the diffuse waves generated by pressure fluctuations in the atmosphere, the scattering of water waves in the ocean, and any number of other sources that pervade our world. Because diffuse waves consist of the superposition of waves propagating in all directions, they appear to be chaotic and random. That appearance notwithstanding, diffuse waves carry information about the medium through which they propagate.
We review the history of diffuse ultrasonic waves in solids with emphasis on recent developments in field-field correlations and their identification with Green's function. The basic principles appear to be well understood now, and the identity between these two waveforms has been proven under a variety of assumed conditions that guarantee a diffuse field. Promise for practical passive imaging is good; nevertheless, measurements sometimes fail to fully agree with theory. We ascribe this in some cases to incomplete convergence - insufficient amounts of data have been processed. In other cases, it is probably because of a lack of perfect diffuseness; ambient nonmultiply scattered fields are often not equipartitioned and imperfectly diffuse.
Complex overburden is responsible for a variety of seismic imaging/4D problems. Sometimes overburden complexity simply prevents us from imaging the deeper subsurface. We are unable to sufficiently sample and accurately build and honor near-surface velocity models. We propose an alternative solution that does not require knowledge of the near-surface velocity model. The price to pay is placing geophones in the Earth below the most complex near-surface part while keeping sources at the surface. Receivers may sit in horizontal or slanted wells, which may be producers/injectors or dedicated side-tracks. Utilizing time reversal logic, we convert surface-to-downhole data into a new dataset with downhole Virtual Sources (VS) located at geophone positions. The resulting VS dataset with both downhole sources and receivers can be conventionally imaged requiring only the bottom portion of the velocity model below the receivers that is more simple to obtain. To illustrate the technique, we show application to one synthetic data set and one field case study.
Noise generated in an ultrasonic receiver circuit consisting of transducer and amplifier is usually ignored, or treated as a nuisance. Here it is argued that acoustic thermal fluctuations, with displacement amplitudes of 3 fm, contain substantial ultrasonic information. It is shown that the noise autocorrelation function is the waveform that would be obtained in a direct pulse/echo measurement. That thesis is demonstrated in experiments in which direct measurements are compared to correlation functions. The thermal nature of the elastodynamic noise that generates these correlations is confirmed by an absolute measurement of their strength, essentially a measurement of the sample temperature.