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Resonance Frequencies in Passive Recordings Map Fracture Systems: Eagle Ford and New Albany Shale Examples

C. J. Sicking*1, J. Vermilye1, 1. Ambient Reservoir Monitoring

Copyright 2019, Unconventional Resources Technology Conference (URTeC) DOI 10.15530/urtec-347

This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Denver, Colorado, USA,

22-24 July 2019.

The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract

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Abstract

Spectrograms computed from passive seismic trace data reveal the presence and types of signals that are

emitted from fractures in reservoirs. The signals are dominated by resonances, are episodic, and are

stimulated in different fractures at different times. Computing the spectrograms over hours and days

allows clock times to be identified that can be used for imaging the active fractures in the reservoirs.

Studies of passive seismic recorded during hydraulic fracturing suggest that these resonances are related

to transmissive fractures and arise from fluid filled fractures. They are present in the trace data during

quiet times, during stimulation times, and during reservoir production times and the resonating fractures

are mapped for all of these conditions. These resonances appear in at least two forms. One type can be

modeled as eigen-vibrations of isolated fractures excited by external forces and present in the data as

dispersive resonance in the spectrograms. The second type appears as non-dispersive resonance

interpreted as turbulent fluid flow moving in and out of connected fractures. By studying their

characteristics, we have been able to identify dispersive and non-dispersive types of resonance in our

data. The dispersive type can be excited in fractures that experience fracture dimension changes and also

in fractures that are experiencing pressure changes within the fracture but do not have fluid flow. Using a

unique data set from the hydraulic stimulation of an Engineered Geothermal System development, we

have identified both types and have examples of the resonance starting as dispersive resonance, changing

to non-dispersive resonance, and then terminating in microearthquake events.

Processing selected frequency and time windows of these emissions allows the resonance waveforms to

be mapped back to their source locations using seismic depth migration. In quiet, pre-stimulation

periods, resonances that show dispersion are more common. During stimulation, when fractures have

been pressurized, non-dispersive resonance is more common perhaps caused by fluid flow in the

fractures. In the transition between these states, previously closed fractures that are intersected by the

stimulation will change their resonance character from dispersive type to non-dispersive type. Recordings

during reservoir flooding or during production demonstrate a dominance of non-dispersive resonance. In

all cases their locations can be mapped using multichannel recording and seismic depth migration

methods.

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Introduction

Past work using passive seismic for mapping transmissive fractures has documented many examples of

mapping 3D fractures that are confirmed by independent data. Examples are shown in Sicking et al.

(2016) and Geiser et al. (2012). For these past projects the source mechanisms for the signal in the passive

seismic was poorly understood and there was a limited body of literature to support the observed results.

There has been a gap between the subsurface images created using episodic seismic waveforms and the

understanding of the source mechanism for those waveforms. Recent advances have been made in

understanding these signals. Liang et al., (2017), and Tary et al., (2014), have published studies

demonstrating that fluid filled fractures in the subsurface support waves that reverberate in the fractures

and resonate for long time periods. The work by Tary et al., (2014), describes the likely source

mechanism for the ambient signals as resonance in fractures and persuasively argues that the resonating

waves are Krauklis waves. Tary et al., (2014) shows resonance data recorded during frack pumping and

describes how the frequency of the resonance varies with the width and the length of the fracture. The

frequency of the resonance increases with fracture width and decreases with fracture length. Examples of

resonance during frack pumping presented here show the frequency changing over time with continued

pumping. Resonance frequencies appear in both the recorded quiet time passive seismic, in the

recordings during pumping, and in recordings during production. Their connections to fracture systems

has been directly demonstrated using passive seismic data recorded during hydraulic stimulation.

Success in using passive seismic for mapping fractures depends on having an appropriate field acquisition

design and doing high-quality noise analysis and suppression. Sicking et al., (2016, 2017) document the

processes and methods for recording and processing passive seismic. With the new developments

documented here, detecting and isolating the waveforms for use in depth imaging and fracture mapping is

accomplished through computing and analyzing spectrograms. The detected waveforms are interpreted as

a combination of signals stimulated by Krauklis waves reverberating in closed fractures and signals

excited by fluid flow in the fractures.

Krauklis waves are guided waves that propagate along fluid filled fractures. Liang et al., (2017), describes

the source of resonance as counterpropagating pairs of Krauklis waves that form standing waves in the

fluid within the fracture that are sensitive to fracture length and aperture. Tary et al., (2014) makes the

case that these reverberating waves are a likely explanation for the resonance seismic signals recorded in

passive seismic data. These observed fracture resonance frequencies are normally less than 100 Hz.

Among others, Tary et al. (2014) have demonstrated that the 10s-of-seconds-long seismic signals often

seen during hydraulic stimulation are generated in fractures and not by path or receiver side effects. Das

and Zoback, (2013a&b) also address this issue. To identify these signals as resonances in fluid filled

fractures, Tary et al., (2014) rely on models established by Chouet, (1986) and others used to describe

volcanic tremor. Tary et al., (2014) divide the resonance signals into two source types: one involves

eigen-vibrations of fixed crack structures that are set up by external forces; the other assumes internal

fluid resonances set up by turbulent flows in and out of evolving cracks where the resonance varies with

the changes in the widths and lengths of the cracks. The fixed crack model requires finite cracks to

generate resonances while the fluid flow model requires an open channel and continuous flow. These

differences may explain the noted differences in the resonance observed during quiet time and those

observed during pumping and production.

Methods

The steps in preparing the trace data for input to the Streaming Depth Imaging (SDI) process include the

removal of broadband stationary noise, the removal of non-stationary surface noise, and killing bad

traces. Sicking et al., (2016 and 2017) document the workflows. SDI breaks the trace data into short time

windows (200 ms) and carries out depth imaging for each time window. The large number of depth

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volumes computed are analyzed using both statistical methods and manual review. After removal of noisy

depth volumes, the remaining depth volumes are summed to compute the final depth volume that covers

the clock time of interest.

Seismic emissions from fractures are episodic and the time of the emissions cannot be predicted.

Computing the spectrograms from the passive recordings identifies the presence and types of resonances

and allows the selection of times of passive recordings to use in mapping transmissive fractures. Through

the use of spectrogram analysis, the number of computed image volumes is greatly reduced.

Results

Examples of resonance and fracture maps for quiet time, reservoir treatment, and for production show that

the transmissive fractures are active in all reservoir states. For the examples shown here the resonance

activity during quiet times is dispersive and episodic while the activity during treatment and production

are dominated by non-dispersive resonance interpreted as excited by fluid flow.

Three days of passive data were recorded in the New Albany shale during quiet time after the well was

drilled but before the stimulation was designed. Two examples of spectrograms are presented here from

these quiet time data that show the signal from the fractures in the reservoir and the expression of surface

noise that can appear to be signal from the reservoir. The example shown in Figure 1 the resonances are

occurring in the range of 60 to 80 Hertz. For quiet time recording it is typical to see the resonance in the

higher frequencies above 50 Hz. All of this resonance energy is mapped into the fracture map presented

in Figure 3. This result illustrates that there is a lot of resonance in the fractures during times before there

is active drilling or production in a reservoir.

The second example from the New Albany shale is shown in Figure 2 and this time window of the

spectrogram has both the dispersive resonance type and times when there is a mono-frequency band at

approximately 45 Hz that comes and goes during the third day of recording. The dispersive energy in the

frequency range of 60 to 80 Hz has been identified as signal from the reservoir and the mono-frequency

energy has been identified as noise. To separate these two types of apparent resonance and verify which

are signals from the fractures, Streaming Depth Imaging (SDI) was used to image the time trace data into

the depth volume. The same depth volume was computed for each one minute of trace time. Analyzing

the computed depth volumes shows that in this example the times with the dispersive resonance contain

signals from the reservoir layer while the times with the mono-frequency contain signals from surface

noise. One depth volume was computed for a time window that contains higher frequency dispersive

resonance but does not have the mono-frequency signal. Another depth volume was computed for a time

window that has the mono-frequency signal. The image volumes are very different and slices of the

volumes at the reservoir depth and vertical slices are shown in the bottom panels of Figure 2. When there

is surface noise coming from a single location on the surface, the linear artifacts in the imaged depth

Figure 1. Spectrogram for 20 minutes during quiet time that shows dispersive resonance in the frequencies above 50 Hz.

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volumes point to the location of the noise source. The depth slice and the vertical slice through the

volume that has the mono-frequency have linear amplitude artifacts that converge on the location of the

noise source. As these surface noise sources often have the mono-frequency expression in the

spectrograms, the noise can be attacked by notching out the very narrow band where they occur. It must

be noted here that valid signal mono-frequency responses frequently occur during treatment and reservoir

flooding operations which is interpreted as fluid flow in the fractures. These mono-frequency responses

must be tested in each case to determine the source of the signal. Projects examined to date show that

non-dispersive resonance does not occur in the reservoir unless there is injection or production activity.

This supports the interpretation of the mono-frequency signals as caused by fluid flow described as

turbulent flow resonance by Liang et al., (2017).

The resonance frequencies that are in the quiet time recordings before the treatment, map out the

transmissive fracture system naturally present in the rocks. Of the 72 hours of passive data recorded for

the New Albany shale, 9 hours were selected for use in the computation of the fracture map near the well.

SDI was run for these 9 hours and the final activity volume is presented in Figure 3. Note that there is

high activity at the toe of the well, the middle of the well has low activity, and the heel of the well shows

higher activity. This activity volume was used to design the stimulation for the stages of the well before

the stimulation project was begun.

It is common to find that the fractures extracted during the treatment match the fractures mapped during

the quiet time before treatment. This is illustrated in Figure 4 for the New Albany shale. The fractures

mapped during the treatment for stage 1 are shown in Figure 4 as the lines overlaid on top of the seismic

Figure 2. Top panel shows a spectrogram for passive data traces recorded in the New Albany shale. The time window on the left does

not have the mono-frequency at 45 Hz. The time window on the right has the 45 Hz mono-frequency. The left panel on the bottom shows the depth slice of the emissions volume over a time window with resonance for signal. The middle and right panels show the

depth slice and a vertical slice of the emissions volume over the time window with mono-frequency in the spectrogram. The slices of

the volume with the mono-frequency show an artifact that has the classic footprint for noise generated on the surface. The black lines

converge on the location of the noise generator.

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emission activity imaged from the pre-treatment quiet times that are shown in color. There is a very close

match of the during treatment stimulated fractures and the natural fractures mapped before treatment.

Several observations were made in the passive data recorded during stimulation in the New Albany shale.

Pump startup and shutdown and changes in the pressure and slurry rate during pumping have very

different responses in the spectrogram resonances. Figure 5 shows the pump curves for the pressure and

slurry rate and the spectrogram that covers the time range from before the pump startup until after the

initial surge in pressure and slurry rate. The spectrogram shows a large impulse in the resonances when

the pumps are started but is delayed by 30 seconds after the change in pressure at the well head. The

resonance attenuation time is on the order of 1 minute after startup. This illustrates the connection

between the pump curves and the resonances. This same observation can be seen in the example of a

pump shut down shown in Figure 6. The impulse in the resonances in Figure 6 are delayed by 15 seconds

from the initiation of the shutdown. The large amplitude resonances after the shutdown continue for 30

seconds.

Figure 7 shows 13 minutes of a spectrogram during pumping. At the beginning of this time period the

pumping was being held relatively constant and the observed resonance has a smaller amplitude than

during the times when the pressure or slurry rate is changing. At seven hours and 24 minutes clock time

the pressure was increased from 4900 psi to 6400 psi. During this time period there are 4 intervals of

Figure 4. Quiet time activity is shown in color and the fractures extracted from the stage 1 stimulation is shown by the lines. The fractures from stage

1 treatment very closely match the pre-treatment quiet time.

Figure 5. Pump startup shows 30 second delay between the pressure

change and the resonance response at the geophones. Figure 6. Pump shutdown shows a 15 second delay between the

change in pressure at the well head to the resonance response at the

geophones

Figure 3. Final activity volume for nine hours of quiet

time data for New Albany shale.

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dispersive resonance in the 40 to 45 Hz band and narrow frequency bands where the resonance does not

show dispersion but has constantly changing amplitude. At approximately 7 hours, 27 minutes and 50

seconds the pressure is maintained at 6400 psi until 7 hours, 33 minutes. During this same time the slurry

rate is dropped from 70 to 35 barrels per minute. This change in pressure and slurry rate causes the

resonance to change to a very different pattern with a few narrow bands that show pulsing amplitudes

with time. The last time period shows that the slurry rate levels off and the pressure is fluctuating while

the patterns in the resonance change to a different set of narrow bands and there is more dispersion.

Two examples from the Eagle Ford show resonance mapping for a time window during pump startup and

for a later time after formation breakdown. Figure 8 shows a spectrogram from the Eagle Ford project for

40 minutes starting at the beginning of pumping for stage 1. The resonance starts out low amplitude and

only a few frequencies are active. The buildup of pressure over 20 minutes initiates more frequencies in

the spectrogram. This example shows that the change in pressure before formation breakdown is

sufficient to activate resonance in transmissive fractures. The lavender lines show the time window for 4

minutes of data used to compute the transmissive fracture system shown in figure 9. All of the time

windows from pump startup until formation breakdown show the same fracture map. The left panel of

figure 9 shows the top down view of the 3D activity volume as an iso-surface. The iso-surface is taken

from the activity volume. The larger fault is prominent in the figure. Smaller transmissive fractures are

indicated by the less prominent iso-surfaces. The right panel shows a map slice of the 3D seismic at the

depth of the well and an overlay of the fractures extracted from the activity volume. The fracture network

for the zone activated by the pumping pressure startup is clearly shown.

Figure 8. The spectrogram for 40 minutes starting at the beginning of pumping for stage 1. The lines show the time

window for the data used to compute the transmissive fracture system shown in figure 11.

Figure 7. the pressure and slurry rate changes have different resonance signatures. Increasing pressure and constant slurry rate are very different from the resonances during constant pressure and decreasing slurry rate.

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The second example from the Eagle Ford shows the spectrogram for a long duration signal (LDS) in the

middle of the stage pumping approximately 2 hours after formation breakdown. Figure 10 shows the

spectrogram in the left panel for 7 seconds covering the time of the LDS and 1.5 seconds of trace data

during that time in the right panel. The location of this LDS is next to the well approximately at the

location of the perfs for stage 2. SDI was used to compute a depth volume for the time window covering

the length of the LDS. All of the energy in the trace data for that time window maps to a very small

location. The amplitude of the LDS in the trace data is sufficiently high to easily identify the waveforms.

The left panel of Figure 11 shows a depth slice through the emissions volume for the LDS overlaid on the

3D seismic. The energy is tightly focused to the zone just next to the well. The right panel of figure 11

shows the vertical slice of the emissions volume through the middle of the zone of focusing and overlaid

on the 3D seismic. The vertical cross section of the focused emissions is very narrow in space. The

vertical extent of the high energy location is extended because the fracture orientation is vertical and may

be stretched because of the lack of aperture available in the geophone acquisition grid.

Figure 10: Spectrogram for 7 seconds of the trace data are shown in the left panel. The trace data for 1.5 seconds of the 6 second event is shown

in the right panel. This event is not double coupled and is P-wave only for the entire 6 seconds.

Figure 9. The map of the transmissive fractures computed using 5 minutes of trace data. The left panel shows a 3D view of the iso-

surfaces looking vertically down on the fractures. The right panel shows the depth slice of the mapped fractures overlaid on the depth

slice of the 3D seismic. The fracture colors show the ampltude of the activity. Red is high and blue is low.

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An Engineered Geothermal System in Finland recorded passive data on a vertical string of geophones

during a stimulation that lasted for 40 days. The geophones were in a well at the depth 3300 meters while

the zone being stimulated in a nearby well was at 6100 meters. This passive seismic is exceptional in that

the geophones are so deep that they do not see any surface generated noise. Trace processing is not

required for these data. It is essentially noise free signal. The spectrograms were computed from the trace

data for 5 days of the recording and show at least two very interesting phenomena. Figure 12 shows one

example of dispersive resonance that lasts for 2.5 minutes and changes to non-dispersive resonance.

Figure 13 shows a buildup of non-dispersive flow resonance that terminates at the occurrence of an

Micro-Earthquake (MEQ). The interpretation of this sequence from dispersive to non-dispersive

resonance and then the MEQ is that the fractures start out as closed cracks and with the increase in

pressure the dimensions of the fractures change to cause the dispersive behavior. As the pressure

increases the fractures open at the ends to allow fluid flow and excite the non-dispersive resonance. After

the MEQ the pressure is released in the fractures and the fluid flow stops. With continued pumping, the

fluid flow begins again until there is another MEQ. The sequence of fluid flow resonance buildup and

release at the MEQ is repeated hundreds of times.

Figure 11: Depth slice and vertical slice from 3D seismic for this LDS. The LDS is in the center of a small syncline. .

Figure 12: Dispersive resonance observed in the deep geothermal passive seismic. The periold of dispersive

resonance changes to a period of continuous narrow band resonance.

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Reservoir activity volumes have been computed from passive seismic recorded for producing wells. A

published example is shown in figure 14 (Sicking, et al., 2016). The producing volume changes as

production declines over time. Time lapse ambient monitoring provides the data for computing the

producing volume over the years of production. The producing volume computed at two different times

over three years for a well in the Eagle Ford is shown in Figure 14. The volume and locations of seismic

emissions along the wellbore in this example were observed to significantly change over this time

interval. Figure 14 shows the SRV and two volumes during production. The SRV is computed for the data

recorded during hydraulic fracturing. The first producing volume was measured after two years of

production and the second after three years of production. There was a substantial decrease in the

producing volume from year 2 to year 3. The production history matches this decline. The calculation in

the third year was from data recorded during a significant drop in production.

Figure 13: This Sequence of resonance energy shows that the pattern of continuous resonance stops abruptly at the occurrence of a MEQ. The top panel show the vertical geophone trace data for one MEQ that that occurs when the resonance stops.

Figure 14. Changes in producing volume over the life of a well in the Eagle Ford calculated from passive seismic. The left panel shows the

activated fractures (SRV) during hydraulic fracturing, the producing volume after two years of production, and after three years of production.

The right panel shows the production curves for the same well. The arrows mark the times of the passive seismic recordings.

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Discussion

Computing spectrograms from passive seismic traces has identified long duration narrow-band frequency

patterns that are interpreted to be caused by resonance in fluid filled fractures. Based on published

literature and our examples, these resonances can be classified as dispersive and non-dispersive types. It

is expected that the dispersive type of resonance indicates Kauklis waves in fractures without significant

fluid flow. This fits the closed crack model and indicative of changing fracture aperture and/or length.

The dispersive resonance can occur for truly closed cracks that are undergoing fracture dimension

changes caused by deformation but can also be stimulated in fractures that are open on at least one end

and pressure is building up in the fracture but fluid flow is not occurring. The non-dispersive type of

resonance is more dominant after formation breakdown during stimulation and indicates that fluid flow is

dominating the excitation of the resonance. Dispersive resonance expresses in the spectrograms as

changing frequency over time while non-dispersive resonance expresses in the spectrograms as constant

narrow band frequency over time. The mono-frequency resonance patterns likely occur when the fracture

is open either on one end or on both ends.

Both types of resonance can occur in the passive trace data simultaneously because different parts of the

reservoir will be in different states of stress caused by different levels of exposure to the stimulation

pressures and fluids and because subsurface fracture networks are complex. The time dimension in the

Freq-Time data shows only the time of the occurrence. In order to find the locations of the resonance one

must use Streaming Depth Imaging and a full aperture of trace data to image the resonance energy to the

correct spatial locations. During stimulation the resonance patterns are sensitive to changes in the

pressure and slurry rate and the changes in observed resonance closely correlates with the changes shown

in the pump curves. Various examples presented in this paper show that these resonances map to focused

spatial locations in the reservoir at depth.

The passive data from an Engineered Geothermal System development project are essentially noise free

and shows both the dispersive resonance during pressure buildup and the transition to non-dispersive

resonance. The interaction of resonance with the occurrence of micro-earthquakes is demonstrated by the

buildup of the continuous narrow band resonances that abruptly terminate at the occurrence of an MEQ.

This is a clear demonstration of pressure cycling in the fractures in the granite. Based on the work of

Liang, et al., (2017), and Tary, et al., (2014), this type of resonance indicates fluid flow in the fractures.

The observed differences between pre-stimulation quiet time resonance and resonance during stimulation

show that during stimulation the resonance amplitude is higher. Only a few minutes of trace data

recorded during stimulation are required to compute fracture maps. Computing fracture maps at different

times during stimulation and accumulating the maps over time allows tracking of the development of the

fractures over time. The quiet time resonance amplitude is weaker and requires more recording time to

map the transmissive fractures.

Conclusions

The examples presented in this paper demonstrate that the source of the signal for the seismic emission

volumes computed from passive seismic are the resonances and in the transmissive fractures. The

resonance signal energy is a substantial component of the total signal emitted from the reservoir for

passive seismic and is the source of the waveforms for the seismic emission volumes that are used to map

the transmissive fractures. The resonance signal maps fracture permeability pathways during pumping

startup before formation breakdown. This is a time during which very few hypocenters or MEQ are

identified. Resonance signal is present in almost all passive data and can be used to map transmissive

fractures in the reservoir during quiet time, during stimulation, and during production. This allows for

better reservoir development and management over the life of the field.

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Acknowledgements

We thank Tero Saarno, manager of St1 Deep Heat Oy and Production project in Helsinki for allowing the

use of the OTN-3 stimulation data for publication. We acknowledge Sergio Valenzuela and the St1 DH

seismology team for their tireless support of the near real-time data acquisition system, analysis, and

contributions toward realizing the goals of realizing a EGS system without causing a damaging

earthquake. We thank Ambient Reservoir Monitoring (ARM) management for supporting this

publication.

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