first break volume 32, July 2014
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Passive Seismic
Increasing the accuracy of microseismic
monitoring using surface patch arrays and a
novel processing approach
P.-F. Roux1*, J. Kostadinovic1, T. Bardainne1, E. Rebel1, M. Chmiel1, M. Van Parys1, R. Macault1
and L. Pignot1 present an acquisition technique to further decrease the noise recorded at the
surface of the Earth when monitoring hydraulic stimulation.
I
t is well known that fluid injection into reservoirs, be
it in the context of enhanced geothermal systems or
for the stimulation of hydrocarbon reservoirs, generates so-called ‘induced’ seismic activity (Evans, 1966).
Early on, the link between the stimulation and this activity has been established, and it has become increasingly
obvious that measuring the microseismicity generated by
the injection would provide a wealth of information on
the mechanical processes at work during the stimulation.
Historically, downhole geophone tools have been used
to monitor microseismic activity during stimulation programmes. Such tools usually offer a very high sensitivity
to the microseismic sources, provided that the observation well is close enough to the treated well (Rutledge and
Phillips, 2003). However, this becomes limited when more
information on the source mechanism (usually termed focal
mechanism and represented by the infamous moment tensor) is required. This is because of the three-dimensional
nature of the focal mechanism, which means it cannot be
retrieved properly using a single observation point. In addition, a poorly situated observation well may indeed lead to
a reduced detection capability.
The last ten years have seen the advent of dense surface
networks to record microseismic activity associated with
hydraulic stimulations (Lakings et al., 2006). Such networks
offer a viable alternative to downhole monitoring, particularly for source coverage (and thus focal mechanism determination). In addition, this technique can offer improved
accuracy in the determination of event epicentres (Eisner et
al., 2010). On the other hand, the depth accuracy of surface
network monitoring is very sensitive to the velocity model
used in the processing of the corresponding data, and its
location at the surface of the Earth, i.e., distant from the
actual sources of microseismicity, tends to severely reduce
its sensitivity to low-magnitude events. Until now, almost all
deployed surface arrays have been deployed in a star pattern
(Duncan and Eisner, 2010) as it is assumed to offer the best
attenuation of surface noise coming from the wellhead.
1
*
In this paper, we present a novel acquisition technique to
further decrease the noise recorded at the surface of the Earth
when monitoring hydraulic stimulation. We argue that the
wellhead is not the only source of noise in such cases; it is in
fact one of the less important ones. Based on this observation,
and building upon recent advances in seismic acquisition, we
have developed a practical and highly efficient acquisition
design, namely a patch array approach. It offers a wide
variety of noise cancellation methods while remaining easy to
deploy in the field. In addition, we have developed a suite of
processing schemes (Macault and Bardainne, 2014; Chmiel
and Bardainne, 2014) that take full advantage of the patch
array design. We will present them herein, and demonstrate
the benefits of such processing techniques within a case study.
Recording microseismic activity using
a surface network
One of the most prominent challenges in surface-based
microseismic monitoring is its susceptibility to noise. Yet, as
pointed out by Scales and Snieder (1998), there is no simple
and single definition of what noise is. A simple and convenient way to define noise is the ‘unwanted part of a seismic
recording’. As a matter of fact, several noise sources exist
that may or not impede the quality of microseismic recordings (Meunier, 2011):
n Digitization-induced noise, caused by rounding error when
the input measure is digitized into an n-bit digital word).
n Thermal or Johnson noise, caused by the thermal agitation
of the charge carriers. It is often referred to as recorder
noise, because most of it is generated in the pre-amplifiers
used in seismic recorders.
n Ambient or seismic noise, which corresponds to waves
propagating from undesired sources located at the surface
of the Earth, and often caused by human-related activities
(traffic, cities, factories, etc.).
While the first two items depend only on the set of instruments used to record the data, the last category comprises
Magnitude (a Baker Hughes – CGG joint venture).
Corresponding author, E-mail: [email protected]
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Figure 1 Power spectral densities of ambient noise
(solid, black line) and instrumental noise (dashed,
black line). The ambient noise has been recorded
using a broad-band seismometer, while the instrumental noise shows the response of a single 10 Hz
geophone connected to a 24 bits, high gain seismic
recorder. At frequencies lower than approx. 3 Hz,
the instrumental noise is higher than the ambient
noise. Above this frequency, however, ambient
noise is high, with a peak at 10 Hz.
‘external’ noise sources and how these interact with the
surface and near-surface conditions of the Earth. In particular,
ambient noise related to human activity is the largest magnitude and the highest in frequency, typically ranging from 1 Hz
up to 30 Hz (Figure 1). It should be noted that the sources of
these noise wavefields are distributed over a wide area and not
necessarily located at a single position, as often assumed during hydraulic stimulation operations. Although the pumps do
generate a large amount of surface waves, they do not impact
more than a small portion (typically approx. 1%) of the monitoring network - see (Drew et al., 2012) as an example. Roads
crossing the lines of the monitoring network, nearby facilities
or cities, and other pads or wells being pumped, all contribute
to increasing the noise at a given site.
The microseismic signal is obviously uncontrollable, but
we do have leverage on the noise – although limited and finite.
The relation between the former and the latter is quantified
through Signal-to-Noise Ratio (S/N), a concept defined by
(Sheriff, 2011) as follows:
‘The energy (or sometimes amplitude) of the signal divided
by all remaining energy (noise) at the time. Sometimes the
denominator is the total energy, that is, S/(S + N). Signal-tonoise ratio is difficult to determine in practice because of the
difficulty in separating out the signal (the desired portion).’
It appears that increasing S/N is paramount to achieving a
complete description of the microseismic activity generated by
stimulation, and boils down to decreasing the amount of noise
that will impede the records. There are two main pathways to
attenuate the noise: processing and acquisition. A wide range
of noise removal methods are available at the processing stage
(f-k, Radon, adaptive/matched filtering being the main ones,
(Forghani-Arani et al., 2013; Wang et al., 2009). Most of these
methods assume that noise is correctly sampled. Similarly,
(Anstey, 1986) demonstrated early on that surface wave-based
noise could effectively be reduced at the acquisition stage
through spatial-filtering – provided that the areal layout of
geophones has an adequate spacing. Should each geophone be
recorded independently, such geophone-arrays can be formed
96
later on at the processing stage (so-called digital array forming,
see e.g., Seeni et al., 2014). But, more importantly, trying not to
record such ambient noise seems to be the most natural way to
increase S/N (Ackerley and Spriggs, 2012).
The patch array (Pandolfi et al., 2013; Auger et al., 2013)
is an acquisition strategy that combines both unaliased noise
sampling and deployment flexibility to avoid noisy areas. A
patch array is a set of small (typically 150 m x 150 m) yet
dense networks of typically 48 traces, each recording a 12-geophone U-shaped array (insets of Figure 2). Each patch can be
seen as an adaptive array acting as a single sensor once stacked,
similar to those found in optical astronomy (e.g., Angel et al.,
1990). These adaptive sensors focus altogether and simultaneously at both patch and network scales on to the hypocentral
region (see the section on Processing patch-array data below).
In an ideal, noiseless situation, patches are located at the
surface to achieve the adequate sampling of the focal sphere
Figure 2 a patch array equally sampling the focal sphere and projected onto
the surface. The inset shows the details of a single patch (in terms of geophones groups).
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Processing patch-array data
Figure 3 example of an adequate focal sphere sampling using a spherical helix.
Each of the dots is propagated up to the surface in a velocity model to provide
the ideal location of a patch.
using a spherical helix as pictured in Figure 3, or any other
sampling function of a sphere (e.g., a geodesic dome). Each
point on the focal sphere is then propagated up to the surface
through ray-tracing (Belayouni et al., 2013). Figure 2 shows
an example of a patch array layout generated with this
methodology. Furthermore, phase variations caused by the
radiation pattern of the microseismic event are of sufficiently
large wavelength to allow each patch to be moved away from
potential noise sources located nearby.
The geometry of each patch can be refined based on prior
knowledge of the ambient noise field and of local, surfacewave propagation characteristics. This is typically done by
satellite map inspection and noise surveying at each planned
location, as well as by analyzing raw shot points from previous surface seismic surveys of the area under scrutiny.
In parallel to the development of an acquisition strategy to
increase the S/N and, consequently, the sensitivity of a surface
network to small events, we have developed a processing methodology that takes full advantage of the patch array design and
allows continuously-recorded data to be processed efficiently
in real time. It should be noted that because the wavefields are
densely sampled at the scale of a single patch, it is possible to
pre-process each patch with denoising procedures such as f-k
or τ – p based methods. In addition, more advanced procedures (such as surface-wave adaptive denoising, Le Meur et al.,
2010) can be applied to further decrease the noise locally, and
therefore increase the overall sensitivity of the patch array to
small-magnitude microseismic events.
Multi-array, relative slant-stacking
The basis of our processing scheme is a delay-and-sum (sometimes termed beam-forming) relative to a template event
(Figure 4a), associated with a grid-search in the region of
interest (Figure 4b). Figure 4a shows how the time shifts corresponding to each test location are applied to the patch array
through its correlation with a template event of known coordinates. For example, work in the wellbore prior to any stimulation (e.g., perforation shots, string shots) can be known with
sufficient accuracy to disregard uncertainties on origin time
Figure 4 The recorded signal is correlated to a template that corresponds to an event of know origin time and location. Doing so, the signal is moved-out and
becomes a relative signal. The space is discretized in test positions (schematized as light grey triangles) around the template’s location. Relative travel times are
applied to the correlation gather which is stacked, until a maximum is found.
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and position. It is therefore possible to generate a template by
using the observed arrival times corresponding to these events,
or even to use the corresponding seismogram directly as a
template. Such a template acts as an empirical Green’s function, and because events situated nearby share almost identical
move-outs (e.g., Spudich and Bostwick, 1987), the correlation
of the raw data with the template aligns the data sufficiently to
yield a coherent stack (Figure 4a). It is interesting to note that
such an operation can be regarded as a time-reversal type of
methodology as a correlation is equivalent to convolving the
data with a time-reversed version of the empirical Green’s
function.
Practically speaking, it is possible to transform the
parameter space (discretized hypocentral region) into a twoor three-dimensional grid of slowness – resulting in a socalled slant stack (Figure 5) by assuming planar propagation:
su (t ) =
1
M
M
Σ d (t – t
i =1
i
u ,i
)
with su the resulting slant-stack for slowness u, di (t) representing the seismogram at receiver i, tu,i representing the
travel time to receiver i for (horizontal) slowness u, and M
is the number of receivers to be stacked (stack-order). Thus,
each grid point of coordinates {xg , y g , z g} can be mapped on
a surface array as a 2D vector of coordinates {u gx , u gy}, or 3D
using three-dimensional arrays. Interestingly, and owing to
some careful approximations, grid-points/patch couples share
many common slowness vectors, thus dramatically decreasing
the actual dimensions of the parameter space to be explored.
This, in turn, allows a dramatic speed-up of the calculation of
so-called focalization maps.
The selection of the event, or events, that are used as
templates and the way they are compared to the actual data
will define the spatial resolution and sensitivity to smaller
microseismic events. In particular, the phase variations at the
surface need to be accounted for to ensure constructive stacking and sufficient denoising to lead to detection. Traditionally,
the focal mechanism’s radiation pattern is not properly
accounted for but rather ignored by signal processing operations using, for instance, characteristic functions such as semblance (Neidell and Taner, 1971), energy, Hilbert envelope,
or a more elaborate summation as in (Rebel et al., 2011),
leading to an inefficient denoising during beam-forming.
Furthermore, using a single event template throughout the
processing of an entire frac job may lead to increasing location bias with increasing distance from the template.
Multiple local signed templates
Using multiple, carefully selected events, including known
sources such as perforation shots, to guarantee location
accuracy, and keeping the information on both travel times
and spatial phase variation caused by focal mechanism radiation diagrams (so-called signed template) can dramatically
98
Figure 5 Schematic principle of slant-stacking. (a) and (b) show two wavefronts impinging the surface network (represented as black inverted triangles)
with two different incidence angles, or slant.
increase both the sensitivity to small-magnitude events, while
ensuring high location accuracy thanks to the relaxed velocity model constraints. Such a methodology has been successfully used in earthquake seismology to detect early aftershocks
often masked by the high energy coda of the mainshock (e.g.,
Lengliné et al., 2012), as well as to determine the relative
location of clustered events (see, for instance, Deichmann and
Garcia-Fernandez, 1992; Got et al., 1994; De Meersman et
al., 2009). This assumes that most events will share identical
or near-identical focal mechanisms. However, observations
from both surface and downhole arrays show that induced
microseismic events tend to group into clusters sharing similar
focal mechanisms throughout a stimulation (e.g., Moos et
al., 2011; Staněk and Eisner, 2013). This is likely to be due
to pre-existing fracture plane orientation with respect to the
local stress conditions. This, in turn, ensures that using a sufficiently large panel of template events will enable the detection
and location of a representative if not exhaustive part of the
induced seismicity.
Case study: monitoring a Cana-Woodford
stimulation programme
We have applied the aforementioned acquisition and processing approaches to a multi-well stimulation programme carried
out in the Cana-Woodford shale. The data set was acquired in
2012 by an array of 35 patches of 48 traces each (Figure 6).
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Figure 6 Patch network (black squares) and treated wells. The red circles show
the location of the three stages that are looked at in the text.
Data were processed in real time using a single, unsigned template approach (Auger et al., 2013). We recently revisited this
data set to assess the gain in both resolution and sensitivity
obtained through the use of local signed templates. A single
template, local to these stages, has been used for the purpose.
We present here the results corresponding to three of the
twelve stages that showed the largest microseismic activity at
the time of the original processing, to provide a good statistical basis for comparison with the new processing.
Figure 7 shows the map of detected and located microseismic events through the initial processing (i.e., using a
remote, unsigned event template, located outside the range of
the shown map). Each stage is colour-coded and perforation
shots are shown as black-outlined circles (with corresponding stage colour) along the wellbore. The stimulation
programme progresses from south to north (from the bottom
to the top of the figure), and each stage is composed of four
perforation clusters. A total of 649 events have been identified throughout the three stages (see Table 1 for stage-wise
figures). Although each microseismic cloud is located close
to its corresponding perforation cluster, no clear geometrical
pattern seems to stand out. Figure 8 shows the same three
stages processed with a local, signed template. The location
of the template event used to process these three stages is
shown as a red star in the cloud of events corresponding
to stage 7. A total of 1326 events have been detected and
located for the three stages. This represents an increase of
more than 42%. Further to this increase, it now appears
that the microseismic clouds emanate from their respective
perforation clusters; in fact, it is even possible to distinguish
which of the clusters is more effective. Such information is
© 2014 EAGE www.firstbreak.org
Figure 7 map view of the micro-seismic activity detected and located using a
remote template. The template used does not appear on this map as it falls
outside of the map’s range. Events are color-coded by stage and sized by
magnitude, and perforations are pictured as colored circles outlined in black.
Earliest stage is situated on the Southern end. Although the microseismic
activity is located in a region corresponding to its perforation cluster, it does
not show a clear geometrical pattern. Most of the activity seems to occur West
of the treated well.
vital when considering advanced stimulation design because
it is generally assumed that all clusters will take an equal
portion of the stimulation (Cipolla and Wallace, 2014).
Figure 9 compares the resulting frequency–size distribution (so-called Gutenberg-Richter plots) obtained through
both datasets. The distributions are modelled taking into
account the detection capacity of the network (Ringdal,
1975). A microseismic event catalogue (a catalogue being a
collection of origin time and location of validated microseismic
events) is indeed intrinsically dependent on the network that
has been used to build it and the noise level at each station of
the network. The basic principles of modelling the detection
capacity of a network then amount to characterizing it with a
detection function that gives the probability that an earthquake
of a given magnitude will be detected. The resulting model
provides both the minimum magnitude below which seismic
Remote template
Local template
Stage 6
179
452
Stage 7
362
482
Stage 8
321
392
Table 1: Break-down of the number of detected and located events per stage
and per processing methodology. Note the overall increase when using a
local template.
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activity is no longer exhaustively recovered (the magnitude of
completeness, mc) and the slope of the linear portion of the
frequency – size distribution curve (the b-value) ). The lower
the magnitude of completeness, the more complete a catalogue
will be – within the limits of the physics relating to the
rupture size and the stimulated volume. Considering our two
datasets, the magnitude of completeness has shifted down from
mc -2.42 to mc -2.56, while the b-value has changed from 2.0 to
1.97. Note that the resulting fit with the modelled curve is also
better at all magnitudes with the reprocessed dataset. Although
these changes are only minor, they demonstrate that using a
local template increases the overall sensitivity of detection and
consequently decreases the magnitude of completeness. This
further demonstrates that the use of a local, signed template
boosts the S/N of the slant stack, thus dramatically improving
the completeness of the catalogue.
Conclusion
Figure 8 map view of the microseismic activity characterized using a local template. The template is shown as a red star. Compared to the previous figure,
the clouds now show clear linear features in connection with the perforation
clusters. The level of detail is sufficient to possibly infer cluster’s performance.
Figure 9 frequency – size distributions (Gutenberg-Richter) for (a) the vintage
processing and (b) the local template processing. The modeled distribution
provides both b-value and magnitude of completeness (see text for details).
The latter is materialized by dotted vertical line at the corresponding magnitude value. While b-values are almost identical, the magnitude of completeness is lower when using the re-processed catalogue than when using the
vintage catalogue (mc -2.56 vs mc -2.42, respectively).
100
We have explained how it is possible to increase the signalto-noise ratio of a microseismic signal recorded during
hydraulic stimulation at both the acquisition and processing stages. In the first part of this article, we presented the
patch array acquisition design based on simple observations
of noise conditions at the surface. This design, coupled
with pre-survey scouting and a proper assessment of the
noise propagation characteristics, can lead to a significant
reduction in the noise (up to 20 dB as reported by Auger
et al., 2013). We then introduced our detection and location procedure that uses local information to reduce the
bias introduced by the velocity modelling and the unknown
geomechanical conditions that may give rise to unexpected
focal mechanisms; to do so, we simply use the travel times
and phase information of local microseismic events as ‘signed
templates’ to locate similar events, based on the observation
that earthquakes generated close to each other share similar
seismograms. Using several such signed templates to account
for the possibly wide variety of sources mechanisms can
significantly increase sensitivity to small-magnitude events.
Comparing them to perforation shots of known locations
helps to assign the microseismic cloud’s barycenter at the
correct position.
Lastly, we demonstrated the effectiveness of these combined acquisition and processing techniques on a real dataset
recorded in the Woodford formation. We presented the initial
results obtained with the use of a single, unsigned template
(Rebel et al., 2011) adapted to a patch array design, and compared them to results obtained with the local, signed template.
The latter showed a dramatic improvement in both sensitivity
and accuracy of the resulting locations.
Acknowledgements
The authors would like to thank the E&P companies who
have anonymously given permission to show these results.
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Passive Seismic
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