Using seismic noise derived from fluid injection wellfor continuous reservoir monitoring
Takeshi Tsuji1, Tatsunori Ikeda2, Tor Arne Johansen3, and Bent Ole Ruud4
Abstract
To construct a reliable and cost-effective monitoring system for injected CO2 in carbon capture and storageprojects, we have considered a seismic monitoring approach using seismic noise from a fluid injection well. Thepassive seismic interferometry continuously monitors injected CO2, enabling the detection of associated acci-dental incidents (e.g., CO2 leakage). We have applied three approaches: (1) crosscorrelation, (2) crosscoher-ence, and (3) deconvolution, to the passive seismometer data acquired during a fluid-injection experiment inSvalbard in the Norwegian Arctic. The crosscoherence approach enabled the construction of shot gathers similarto active-source data. Reflectors from the reservoir could be identified on common-midpoint (CMP) gathers con-structed via seismic interferometry, and seismic velocity could be estimated from the time-lapse CMP gathers.High-frequency noise from fluid injection operations and low-amplitude background ambient noise were suitablefor reconstructing virtual seismic data. However, we clearly found that the time variation characteristics of thenoise influencedmonitoring results, and thus the stable part of the noise should be used for monitoring. We furtherapplied surface-wave analysis to the virtual shot gathers derived from seismic interferometry and investigatedvariations in S-wave velocity structure in a shallow formation. We observed clear time variations in seismic veloc-ity in the shallow part of permafrost regions. The information derived from the surface-wave analysis is useful inevaluating the influence of shallow formations on monitoring results of deep reservoirs.
IntroductionIn carbon dioxide capture and storage (CCS), the
monitoring of injected CO2 is crucial for (1) predictingthe risk of CO2 leakage from storage reservoirs, (2) in-creasing the efficiency of CO2 injection and reducing thecost, and (3) reducing the risk of injection-induced seis-micity. Through monitoring techniques, we can estimatethe time variation of CO2 saturation and pore pressurewithin the reservoir (Chadwick et al., 2009). By applyingmonitoring-derived information to reservoir simulations,we can predict the future CO2 distribution and areas ofpotential CO2 leakage. Furthermore, accurate monitor-ing of variations in pore pressure due to CO2 injectionis an important input to geomechanical modeling to pre-vent generation of injection-induced earthquakes. There-fore, reliable, continuous, and cost-effective monitoringmethods are heavily required in CO2 storage.
As a CO2 monitoring method, time-lapse seismicsurveys have been used to determine injected CO2 dis-
tribution (Chadwick et al., 2009). Using this method, wecan reveal the spatial distribution of the injected CO2even at a relatively low CO2 saturation. Because P-wavevelocity decreases dramatically as the CO2 starts to in-vade pore spaces of rocks initially saturated with brine(Kim et al., 2011; Yamabe et al., 2016), changes in thereflection characteristics of time-lapse seismic data canbe used to evaluate the distribution of injected CO2. Inongoing CCS projects, time-lapse seismic data havesuccessfully revealed the spatial distribution of CO2(White, 2013). The CO2 storage operation at Sleipner inthe North Sea provides an excellent demonstration ofthe application of time-lapse seismic methods to mon-itoring CO2 plumes (Arts et al., 2002; Chadwick et al.,2009). In this CCS project, CO2 plumes are imaged asseveral bright subhorizontal reflections within the res-ervoir, growing with time. The plumes also produce avelocity pushdown caused by reduced velocity withinthe CO2-saturated rocks. Recent Sleipner data sets
1Kyushu University, International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Fukuoka, Japan and Kyushu University, Facultyof Engineering, Fukuoka, Japan. E-mail: [email protected].
2Kyushu University, International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Fukuoka, Japan. E-mail: [email protected].
3University of Bergen, Department of Earth Science, Bergen, Norway and University Centre in Svalbard, Longyearbyen, Norway. E-mail: [email protected].
4University of Bergen, Department of Earth Science, Bergen, Norway. E-mail: [email protected] received by the Editor 1 February 2016; revised manuscript received 4 April 2016; published online 5 August 2016. This paper appears
furthermore showed that abnormal reflections within adeep plume are fading out, suggesting changes in super-critical CO2 saturation in the deeper parts of the plume(Chadwick et al., 2010). In conventional geophysicalmonitoring, however, the interval of the time-lapse sur-veys is usually long due to their cost. The continuousmonitoring of the dynamic CO2 behavior can contribute
to suitable reservoir management, and it should also becrucial for detecting accidental incidents associatedwith CO2 injection (e.g., leakage).
In this study, we consider a monitoring approach byapplying seismic interferometry to seismic noise data ac-quired during a fluid-injection experiment (injectivitytest) at the UNIS CO2 Lab in Svalbard, Norway (Figures 1
and 2; Braathen et al., 2012; Lecomteet al., 2014). Seismic interferometry canbe used to retrieve the Green’s functionbetween two receivers from the ambientnoise. Thus, we could construct time-lapse seismic data using only passiveseismometer data, which are usuallyacquired for microseismic detection. Be-cause no additional seismic data wererequired, this method is particularly at-tractive for cost effective, continuousmonitoring (Draganov et al., 2012; Min-ato et al., 2012a). Previous studies haveproposed the application of seismic inter-ferometry to CCS projects (Draganovet al., 2012). Some previous studies usingthis approach detected changes in seis-mic velocities in shallow formationsassociated with seasonal variations (Me-ier et al., 2010). To evaluate the influenceof changes in velocities of the shallowformations on monitoring results, wefurther conducted surface-wave analysisusing the noise data to estimate tempo-ral variations in the S-wave velocities ofthe shallow formations. Alterations inseismic velocities in the shallow forma-tions may disturb reflection signals (e.g.,velocity pushdown) from the deeper res-ervoir horizon and thus disturb the mon-itoring results. Accurate S-wave veloc-ities are crucial for the construction ofthe initial geologic models at CO2 storagesites for reservoir fluid simulation andgeomechanical simulation (Ikeda andTsuji, 2015), and its time variation is im-portant for the subsequent microseismic-ity analysis.
Field dataSeismic data were acquired for
detecting microseismicity during a fluidinjection experiment as a site close toLongyearbyen, Svalbard, in the Norwe-gian Arctic (Figure 1). The site comprisesa layered sedimentary sequence (Fig-ure 2) with fluvial sediments comprisingthe shallow part of the formation. Thereservoir is considerably underpressured(approximately 30% hydrostatic pres-sure; Braathen et al., 2012; Oye et al.,2013), but the reason for this has not
Figure 1. Map of the fluid injection site in Longyearbyen, Svalbard, Norway.Two seismic lines (red lines) intersect at the injection well (yellow star). Thenortheastern part of line 2 is over the river.
Figure 2. Active-source seismic profiles of (a) line 1 (northwest–southeast di-rection) and (b) line 2 (northeast–southwest direction). The locations of theseprofiles are displayed in Figure 1. These seismic profiles were constructed usingactive source data. The seismometers are the same as those we used in thisstudy. Yellow stars indicate the position of water injection.
SQ2 Interpretation / November 2016
Dow
nloa
ded
08/0
8/16
to 1
29.1
77.5
5.58
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
been clearly identified yet. Accordingly, water was in-jected with fairly low pressure (up to 40 bar pump pres-sure). Core samples of the target reservoir indicated tightrocks with sandstones of moderate porosity (5%–18%)and low permeability (maximum 1–2 mD; Braathen et al.,2012). However, hydrologic experiments revealed thatthe crustal-scale permeability of the target lithology hasa much higher value (Braathen et al., 2012). The scaledependency of permeability between core measure-ments and logging data indicates that the presence oflarge-scale fractures is crucial for increased fluid flowin the rock succession.
We use continuous seismic records (i.e., passive re-cords) for approximately four days (Figure 3a), and dur-ing the last two days fluid injection was conducted.Along two seismic lines intersecting the injection well(Figure 1), 96 geophones were deployed (48 seismome-ters for each line). These two survey lines were per-pendicular to each other. In this study, we will mainlyshow the results from line 1 (northwest–southeast line;Figure 1). The receiver interval is 25m, the length of eachsurvey line is 1200 m, and the sampling interval is 2 ms.During water injection, the recorded seismic data cap-
tured a wide range of frequencies (with specific high-frequency components), although an approximately 50 Hznoise component was dominant (Figure 3b and 3c).Waveform amplitudes near the injection well werestronger (Figure 3d), suggesting that the seismic noisewas mainly generated from the fluid injection well.
Active-source seismic data were also acquired alongthe two survey lines. By using these data (Figure 4a), wecould validate the results obtained using seismic inter-ferometry. The reflection seismic profiles derived fromactive-source data (Figure 2) clearly indicate a layeredsedimentary sequence from the surface to the reservoir(the yellow star in Figure 2).
Methods and resultsTime-lapse seismic data via interferometry
The concept of seismic interferometry was proposedby Claerbout (1968), who shows that the reflection re-sponse of a 1D medium can be obtained from an auto-correlation of the transmission response. Several recentstudies have demonstrated methods for extracting theseismic impulse response (Green’s function) betweenmultireceivers using seismic interferometry (Campillo
Figure 3. (a) The waveforms recorded at channel #1 for approximately four days. (b) Comparison of waveforms before andduring water injection (2 s). The vertical axes (amplitude) are differently scaled. (c) The time variation of the amplitude spectrumof the waveform recorded at channel #10. Warmer color indicates greater amplitude. (d) Example of recorded waveforms (ap-proximately 1 s) around the injection well. Here, we preserve the absolute amplitude. A high amplitude was recorded near theinjection well, suggesting that almost all noise was derived from the injection well.
Interpretation / November 2016 SQ3
Dow
nloa
ded
08/0
8/16
to 1
29.1
77.5
5.58
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
and Paul, 2003; Schuster et al., 2004; Snieder, 2004;Roux et al., 2005; Wapenaar and Fokkema, 2006; Dra-ganov et al., 2009; Minato et al., 2011). Here, we use(1) crosscorrelation, (2) crosscoherence, and (3) decon-volution to estimate the seismic response between thereceivers (Figure 4b–4d). Crosscorrelation providesamplitude information of a source wavelet, whereas theother methods tend to remove the influence of theamplitude. Using the crosscoherence, the amplitudesare normalized in the frequency domain, and onlyphase is used. Thus, it can be considered as prewhit-ened crosscorrelation process. Details of these threeapproaches are thoroughly given in Nakata et al.(2011). In our analysis, we define the receivers nearestto the injection well as virtual sources because thenoise from the injection well was dominant (Figure 3d).To remove dominant noise at approximately 50 Hz(Figure 3d), we apply a 45 Hz low-pass filter for each
virtual shot gather (Figure 4b–4d). In Figure 4, we fur-ther show the virtual shot gathers of a limited frequencyband (25–45 Hz; Figure 4e–4g), to evaluate the variabil-ity of the retrieved shot gathers with respect to fre-quency.
The characteristics of the virtual shot gathers ob-tained using these three methods (Figure 4b–4d) areclearly different. The shot gathers reconstructed fromthe crosscorrelation (Figure 4b) are dominated by lowfrequencies. In the shot gather constructed by deconvo-lution (Figure 4d), it is difficult to locate events at faroffsets. Comparing these shot gathers with those de-rived from active-source data (Figure 4a), we see thatthe shot gather derived using crosscoherence (Fig-ure 4c) is most consistent with the active-source shotgather. Direct and surface waves are clearly visible inthe crosscoherence-derived shot gathers. The gradientof the direct wave seen in the shot gathers (Figure 4c)
indicates the P-wave velocity near thesurface to be approximately 3300 m∕s.The high P-wave velocity can partiallybe explained by the effect of permafrostas the P-wave velocity of pure ice is3466 m∕s (Kim et al., 2010).
Crosscorrelation or crosscoherencefunctions have causal (positive-time) andanticausal (negative-time) sides, corre-sponding to the wave traveling from thefirst receiver to the second and from thesecond receiver to the first, respectively(Figure 4b and 4c). If the sources ofambient noise are homogeneously dis-tributed, the crosscorrelation and cross-coherence functions are symmetric withrespect to (causal and anticausal) time(Emoto et al., 2015). In this study, weconsider only the causal part for theanalyses because the seismic interferom-etry results captured physical eventsonly in the causal parts as the sourceswere located close to the injection well.
From the continuously recorded seis-mic noise data, we reconstruct time-lapse data using seismic interferometry(i.e., crosscoherence approach; Figure 5).The characteristics of the obtained vir-tual seismic traces clearly vary duringthe approximately four day observationperiod. Virtual seismic traces were, how-ever, poorly estimated from passive datarecorded before the water injection be-gan, although the presence of surfacewaves can be seen. Subsequent to thewater injection, clear direct and reflectedwaves can be identified in the virtualgathers. Temporal variations of the seis-mic traces are strongly related to varia-tions in the characteristics of noisesource.
Figure 4. (a) Active-source shot gather of line 1, and the virtual shot gathers de-rived from noise using (b) crosscorrelation, (c) crosscoherence, and (d) deconvo-lution. The shot gathers in (e-g) are also derived from noise, but the band-limitedfilter (25–45 Hz) was applied. We assume that the shot point is located at thereceiver closest to the injection well. Because of source localization, the seismicsignal appears only in the causal part of the retrieved seismic-interferometry re-sults (positive time). The virtual shot gathers derived from crosscoherence (c andf) are consistent with the active-source shot gather (a).
SQ4 Interpretation / November 2016
Dow
nloa
ded
08/0
8/16
to 1
29.1
77.5
5.58
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
Repeating the analyses for several receiver pairs, weconstruct common-midpoint (CMP) gathers (Figure 6),which show vague indications of reflection events alonghyperbolic curves at near offset (dashed curves in Fig-ure 6). We could not retrieve reflectionevents at the far offset (Figure 6) be-cause the source is localized at the injec-tion well. The inhomogeneous sourcedistribution violates an assumption forthe use of seismic interferometry (i.e.,homogeneous distribution of seismicsources). By considering the raypath(i.e., the stationary phase zone; Sniederet al., 2006; Schuster, 2009), the localizednoise source can effectively generateshot gathers, but it is difficult to recon-struct the CMP gathers. Because thenoise from the injection well containsmultiples and other scattered waves,we construct the CMP gather from onelocalized noise source (Draganov et al.,2009). Although there is a difficulty in re-constructing the CMP gather, the P-wavevelocity estimated from the CMP gatherswas approximately 3350 m∕s for thereflection event at a traveltime of approx-imately 0.33 s, and this velocity is consis-tent with the velocity estimated fromwell-log data and active-source seismicdata (Oye et al., 2013). This result dem-onstrates that subsurface reflectionsaround the reservoir can be extractedusing seismic interferometry analysis.Figure 6 shows two CMP gathers calcu-lated at different times, in which similarreflections could be observed. The reflec-tors with similar characteristics canbe observed on the CMP gather recon-structed during the water injectionperiod. Using such CMP gathers, we canconstruct time-lapse seismic profiles andestimate variations in P-wave velocity. Asdescribed above, these gathers were alsoinfluenced by the temporal variations ofsource characteristics (i.e., before andduring water injection).
The dispersion curve of a surfacewave was estimated from the virtualshot gathers using the crosscoherenceapproach (Figure 4c) through the multi-channel analysis of surface waves (Fig-ure 7a; Park et al., 1998, 1999; Tsuji et al.,2012; Ikeda et al., 2015). The dispersioncurves derived from the surface-waveanalysis are accurately estimated, andwe believe that they are not significantly
influenced by time variations of noise associated withthe fluid injection experiment (see Figure 7). This is be-cause we focus on the dispersion curve within the low-frequency range; the time-variant noise from the fluid
Figure 5. (a) Time-lapse shot gathers from 16 to 18 February using a crosscoher-ence approach. (b) The time variation of crosscoherence between one receiverpair. A clear direct- and reflection waves are identified during water injection be-cause the noise derived from injection well enhances the S/N.
Figure 6. Time-lapse virtual CMP gathers. In these profiles, we filtered out low-frequency contents to enhance the reflection wave. Using these CMP gathers, wecan roughly estimate the time variation of seismic velocities (dashed lines).
Interpretation / November 2016 SQ5
Dow
nloa
ded
08/0
8/16
to 1
29.1
77.5
5.58
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
injection operation is dominant in the higher frequencyrange (Figure 3c). When we performed the time-lapseanalysis of dispersion curves from 15 to 18 February,we observed a slight decrease in the phase velocitieswith time (Figure 7b). Considering the wavelengthsof the surface waves (Heisey et al., 1982), the estimateddispersion curves (>2 Hz) are sensitive to velocity var-iations at shallower depths than the water injectionpoint (<250 m in depth).
Theoretical dispersion curves are calculated fromP- and S-wave velocities and density structures usingthe compound matrix method (Saito and Kabasawa,1993). By fitting the theoretical dispersion curve tothe observed dispersion curve, we estimate the S-wavevelocity (Figure 8). In this study, we use a genetic in-version algorithm (GA; Yamanaka and Ishida, 1996;Tsuji et al., 2012) because the GA avoids all assump-tions of linearity between the observables and the un-knowns; therefore, it does not depend on the reference(initial) velocity model (Socco et al., 2010). The effectsof varying S-wave velocity on the phase velocity ofRayleigh waves are dominant over those caused byvariations in P-wave velocity and density (Xia et al.,1999); therefore, we only perturb the S-wave velocityin the inversion, whereas P-wave velocity and densityare estimated using empirical relationships betweenthe P- and S-wave velocities (Kitsunezaki et al.,1990) as well as the S-wave velocity and density (Lud-wig et al., 1970). A reference S-wave velocity modelconsisting of six layers was initially made by approxi-
mating the S-wave velocity by 1.1 × C, where C is thephase velocity of the Rayleigh wave, and one-third ofthe observed wavelength into depth (Heisey et al.,1982; Tsuji et al., 2012). By applying the inversion tothe time-lapse dispersion curves (Figure 7b), the timevariation of the S-wave velocity shallower than 200 m isinferred (Figure 8). We perform GA inversion 10 timeswith different initial populations for each dispersioncurve. In the inversion, we did not search for the S-wavevelocity for the shallowest layer (0–20 m), but kept itfixed at 1.1 × 350 m∕s because the phase velocity atthe higher frequency range is approximately 350 m∕s.Also, the higher modes included in the considered fre-quency range generally make it difficult to obtain stableresults for monitoring purposes (Ikeda et al., 2012). TheS-wave velocity in the shallow region (<30 m) was esti-mated to be less than 250 m∕s using reflection analysisduring the summertime (Lecomte et al., 2014). Althoughthe S-wave velocity is different from our estimationbased on surface waves, the difference could be mainlydue to seasonal variations. The temperature duringacquisition of our data was approximately −15°C (Fig-ure 8d); thus, the shallow formation water was partiallyfrozen. Indeed, the estimated S-wave velocity varied dur-ing the observation period 15–18 February 2012 (Fig-ure 8a–8c).
DiscussionSuitable environment for reservoir monitoringusing seismic interferometry
The shot gathers obtained by com-puting the crosscoherence of seismicnoise (Figure 4c and 4f) are similar tothe active-source gathers (Figure 4a).In general, because gathers derived fromambient noise are dominant in the low-frequency part of the signal, it was diffi-cult to obtain results similar to the active-source seismic data. At the injection site,the general background noise level islow, whereas vibrations during the waterinjection generated high-frequency com-ponents around the injection well (pump;Figure 3c). The seismic noise includinghigh-frequency components generatedat the injection well (pump) is suitablefor retrieving virtual seismic data for re-flection analysis (Figure 4). However, theobserved amplitude spectrum shows sig-nificant frequency variation and includesspiky noise (Figure 3c). Because thecrosscorrelation approach does not com-pensate for the narrow-band propertiesof the noise sources (Nakata et al., 2011),narrow-bandwidth signals are dominantin the crosscorrelation gathers (Figure 4band 4e). On the other hand, the shot gath-ers obtained via crosscoherence includea wide frequency range because of ampli-
Figure 7. (a) Example of the dispersion curve derived from surface-wave analy-sis for the northwest part of line 1. (b) The time variation of phase velocity (color).The horizontal axis indicates the frequency as in the case of panel (a). The verticalaxis indicates the time (approximately four days). (c) Time variation of phasevelocity at three frequencies. Horizontal axes indicate time (approximately fourdays), and vertical axes are phase velocity.
SQ6 Interpretation / November 2016
Dow
nloa
ded
08/0
8/16
to 1
29.1
77.5
5.58
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
tude normalization (Figure 4c and 4f), and thus, showsimilarities to active-source gathers (Figure 4a). Proxi-mal to the injection site, the ambient noise from humanactivities (e.g., traffic noise) is generally low, whereasthe noise from the fluid injection operations should bedominant. Accordingly, the stationary phase methodshould be selected for the seismic interferometry ap-proach in such environments (Snieder et al., 2006; Schus-ter, 2009; Minato et al., 2012b). The offshore reservoir(seafloor) could be a similar case, in which noise withhigh-frequency components could be generated only atinjection wells (or platform; Mordret et al., 2013). There-fore, this approach using noise mainly derived from thewell could be effective for evaluating the injected CO2 insubseafloor reservoirs.
In contrast, our results clearly demonstrate that thetime variation of the noise (i.e., frequency component)significantly influences the results of seismic interfer-ometry (Figure 5c); the reflection amplitudes fromthe reservoir vary because of noise source conditions.Therefore, we need to carefully check the characteristicof noise for monitoring applications, by using sensorsdedicated for measuring the vibrations generated by
the noise source. If the dominant noise (e.g., pumpingnoise) is time invariant and stable, the noise sourcecould be used effectively for monitoring purposes.Recently, a continuous and controlled seismic sourcepermanent reservoir monitoring (Ayeni, 2010; Ikedaet al., 2015). Ikeda et al. (2015) use the continuous seis-mic source system repeatedly generating sweep wave-forms with a wide-frequency range (including highfrequency). Although each sweep signal has low ampli-tude, many stacking processes largely improve the sig-nal-to-noise ratio (S/N) of the retrieved seismic traces.The high stability of the continuous signals makes it fea-sible to detect subtle changes in surface-wave phasevelocities. Our results demonstrate that such well-controlled source systems are useful for continuousreservoir monitoring.
Influence of surface variation upon monitoringresults
Seismic velocities in the shallow formations are timevarying to environmental conditions at the surface: rain(saturation), temperature (freezing), vegetation, or seis-mic data acquisition or fluid injection operations. In-
Figure 8. Time variation of S-wave velocity in the northwest part of line 1. (a) Time variation of S-wave velocity profiles obtainedvia GA inversion for the dispersion curves. (b) The time variation of the S-wave two-way traveltime from the surface to 200 m indepth. The error bars show standard deviations computed from 10 inverted velocity models with different initial populations. TheS-wave two-way traveltime is varied by approximately 0.05 s during the observation. (c) The time variation of the P-wave two-waytraveltime from the surface to 200 m in depth. The color in (a-c) indicates the observation time shown in color bar. (d) Temperaturevariation at surface for same time slot.
Interpretation / November 2016 SQ7
Dow
nloa
ded
08/0
8/16
to 1
29.1
77.5
5.58
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
deed, surface-wave analysis in this study clarified thatseismic velocity slightly varies over a few days of obser-vation (Figure 8). The S-wave velocity changes signifi-cantly with the degree of freezing (Zimmermann andKing, 1986; Jacoby et al., 1996; Johansen et al., 2003);thus, variations in the S-wave velocity within the fluvialsediments can be explained by the degree of freezingand pore-space saturation. Indeed, the P- and S-wavevelocities (Figure 8b and 8c) decrease subsequent to in-creasing temperature (e.g., 10:00, 16 February or 14:00,17 February in Figure 8d), although their interrelation isnot very clear. However, seismic velocities and temper-ature are gradually decreasing during the monitoringperiod (Figure 8). The long-period time variation cannotbe explained by the degree of freezing. Therefore, theobserved change in seismic velocities can also be attrib-uted to other factors, such as the surface operations as-sociated with fluid injection.
We evaluate the influence of the alteration in surfacevelocity on the monitoring results of deeper reservoirsfor CO2 injection. Our results indicate that the S-wavevelocity decreases during the fluid injection experi-ment. Based on the observed S-wave velocity variation,we estimate the corresponding change in the two-waytraveltime of the S-wave traveling from the surface to200 m depth (Figure 8b). The traveltime changes byapproximately 50 ms during the observation period.Furthermore, by using the relationship between theP- and the S-wave velocities suggested by Kitsunezakiet al. (1990), we calculate the traveltime variation ofthe P-wave velocity from the surface to 200 m as ap-proximately 10 ms (Figure 8c). This variation corre-sponds to an approximately 5% change in the P-wavevelocity. Such changes in seismic velocity considerablyinfluence the monitoring results; the traveltime of thereflector of deep reservoirs could be shifted by 10 ms.Therefore, monitoring velocity variations in the shallowformations is important for monitoring purposes of thedeeper reservoir. In particular, the seismic velocities inthe permafrost region significantly change as meltingpermafrost causes considerably a decrease in the seis-mic velocities. Although we did not analyze ambientnoise during the summer time, the S-wave velocities es-timated from reflection data analysis conducted duringsummer time were much lower (Lecomte, 2014). Thus,seasonal variations in seismic velocities at shallow on-shore formations would have significant impact on themonitoring results (velocity pushdown) of deep reser-voirs. Our approach using the surface waves is suitablefor monitoring shallow geologic formations.
ConclusionsWe considered seismic interferometry using seismic
noise as a monitoring method. This approach has lowcost and possibly makes it particularly attractive forlong-term, continuous monitoring of CCS projects. Weapplied this method to analyze the water injection ex-periment in Norway. Our main observations from thisstudy are
1) The virtual shot gathers derived from noise via thecrosscoherence analysis are similar to the active-source shot gathers. However, narrow-bandwidthsignals are dominant in crosscorrelation gathers be-cause the crosscorrelation approach does not com-pensate for the narrow-band properties of the noisesource.
2) The P-wave velocity estimated from the time-lapseCMP gathers derived from noise is consistent withthe downhole-logging data.
3) High-frequency noise derived from the fluid injec-tion is suitable for reconstructing approximate shotgathers. In remote areas or offshore environmentswhere the noise associated with human activitiesis not dominant, the vibration derived from anyoperations can be dominant. Therefore, we shouldcarefully consider the stationary phase method forretrieving virtual seismic data.
4) Characteristics of noise source significantly influ-ence monitoring results via seismic interferometry.Therefore, the recently proposed continuous andcontrolled source system (active system) for generat-ing a continuous sweep waveform can be an effectivetool for continuous monitoring of deep reservoirs.
5) The surface-wave analysis applied to virtual shotgathers is the most reliable means for identifyingvelocity variations within the shallow formations.The time-lapse S-wave velocity profiles show thatthe S-wave velocities vary through the monitoringperiod. In the frozen shallow sediments, the varia-tions in seismic velocities due to varying freezingconditions clearly influence the monitoring resultsof time-lapse effects caused by injection into thedeeper geologic formations.
AcknowledgmentsWe thank the UNIS CO2 Lab for making the seismic
data and the water injection data available to us. Thisstudy was supported by the Japan Society for the Pro-motion of Science through a Grant-in-Aid for ScientificResearch on Innovative Areas (no. 15H01143); a Grant-in-Aid for Scientific Research (S) (no. 15H05717); aGrant-in-Aid for Science Research (B) (no. 15H02988);and Bilateral Joint Research Projects with the Ministryof Scientific Research, Egypt, and with the Japan Inter-national Collaboration Agency and Japan Science andTechnology Agency through the SATREPS project. Weacknowledge the support of the UNIS CO2 Lab, andI2CNER, sponsored by the World Premier InternationalResearch Center Initiative, Ministry of Education, Cul-ture, Sports, Science, and Technology, Japan.
ReferencesArts, R., R. Elsayed, L. van der Meer, O. Eiken, S. Ostmo, A.
Chadwick, G. Kirby, and B. Zinszner, 2002, Estimationof the mass of injected CO2 at Sleipner using time-lapseseismic data: 64th Annual International Conference andExhibition, EAGE, Extended Abstracts, H16.
Braathen, A., K. Bælum, H. H. Christiansen, T. Dahl, O.Eiken, H. Elvebakk, F. Hansen, T. H. Hanssen, M. Joc-hmann, T. A. Johansen, H. Johnsen, L. Larsen, T. Lie, J.Mertes, A. Mørk, M. B. Mørk, W. Nemec, S. Olaussen, V.Oye, K. Rød, G. O. Titlestad, J. Tveranger, and K. Vagle,2012, The Longyearbyen CO2 Lab of Svalbard, Norway-initial assessment of the geological conditions for CO2
sequestration: Norwegian Journal of Geology, 92, 353–376.
Campillo, M., and A. Paul, 2003, Long-range correlations inthe diffuse seismic coda: Science, 299, 547–549, doi: 10.1126/science.1078551.
Chadwick, R. A., D. Noy, R. Arts, and O. Eiken, 2009, Latesttime-lapse seismic data from Sleipner yield new insightsinto CO2 plume development: Energy Procedia, 1, 2103–2110, doi: 10.1016/j.egypro.2009.01.274.
Chadwick, R. A., G. Williams, N. Delepine, V. Clochard, K.Labat, S. Sturton, M.-L. Buddensiek, M. Dillen, M.Nickel, A. L. Lima, R. Arts, F. Neele, and G. Rossi, 2010,Quantitative analysis of time-lapse seismic monitoringdata at the Sleipner CO2 storage operation: The LeadingEdge, 29, 170–177, doi: 10.1190/1.3304820.
Claerbout, J. F., 1968, Synthesis of a layered medium fromits acoustic transmission response: Geophysics, 33,264–269, doi: 10.1190/1.1439927.
Draganov, D., X. Campman, J. Thorbecke, A. Verdel, and K.Wapenaar, 2009, Reflection images from ambient seis-mic noise: Geophysics, 74, no. 5, A63–A67, doi: 10.1190/1.3193529.
Draganov, D., K. Heller, and R. Ghose, 2012, MonitoringCO2 storage using ghost reflections retrieved fromseismic interferometry: International Journal of Green-house Gas Control, 11, S35–S46, doi: 10.1016/j.ijggc.2012.07.026.
Emoto, K., M. Campillo, F. Brenguier, X. Briand, and T.Takeda, 2015, Asymmetry of coda cross-correlationfunction: Dependence of the epicenter location: Geo-physical Journal International, 201, 1313–1323, doi:10.1093/gji/ggv081.
Heisey, J. S., K. H. Stokoe, II, W. R. Hudson, and A. H.Meyer, 1982, Determination of in situ shear wave veloc-ities from spectral analysis of surface waves: Researchreport, 256-2, Center for Transportation Research, TheUniversity of Texas at Austin.
Ikeda, T., T. Matsuoka, T. Tsuji, and K. Hayashi, 2012, Mul-timode inversion with amplitude response of surfacewaves in the spatial autocorrelation method: Geophysi-cal Journal International, 190, 541–552, doi: 10.1111/gji.2012.190.issue-1.
Ikeda, T., T. Matsuoka, T. Tsuji, and T. Nakayama, 2015,Characteristics of the horizontal component of Ray-leigh waves in multimode analysis of surface waves:
Ikeda, T., and T. Tsuji, 2015, Advanced surface-wave analy-sis for 3D ocean bottom cable data to detect localizedheterogeneity in shallow geological formation of a CO2
storage site: International Journal of Greenhouse GasControl, 39, 107–118, doi: 10.1016/j.ijggc.2015.04.020.
Jacoby, M., J. Dvorkin, and X. Liu, 1996, Elasticity of par-tially saturated frozen sand: Geophysics, 61, 288–293,doi: 10.1190/1.1443951.
Johansen, T. A., P. Digranes, M. van Schaack, and I. Lønne,2003, Seismic mapping and modeling of near-surfacesediments in polar areas: Geophysics, 68, 566–573,doi: 10.1190/1.1567226.
Kim, K. Y., J. Lee, M. H. Hong, Y. K. Jin, and H. Shon, 2010,Seismic and radar investigations of Fourcade Glacier onKing George Island, Antarctica: Polar Research, 29,298–310, doi: 10.1111/j.1751-8369.2010.00174.x.
Kim, J., T. Matsuoka, and Z. Xue, 2011, Monitoring anddetecting CO2 injected into water-saturated sandstonewith joint seismic and resistivity measurements: Explo-ration Geophysics, 42, 58–68, doi: 10.1071/EG11002.
Kitsunezaki, C., N. Goto, Y. Kobayashi, T. Ikawa, M. Ho-rike, T. Saito, T. Kurota, K. Yamane, and K. Okuzumi,1990, Estimation of P- and S-wave velocities in deep soildeposits for evaluating ground vibrations in earthquake:Sizen-saigai-kagaku, 9, 1–17.
Lecomte, I., U. Polom, G. Sauvin, B. O. Ruud, H. Christian-sen, and G. Gilbert, 2014, Shear-wave reflection-seismicpilot study at the UNIS CO2 Lab site, Longyearbyen,Svalbard: 76th Annual International Conference and Ex-hibition, EAGE, Extended Abstracts, We P04 05.
Ludwig, W. J., J. E. Nafe, and C. L. Drake, 1970, Seismicrefraction: In the sea: Wiley-Interscience.
Meier, U., N. Shapiro, and F. Brenguier, 2010, Detectingseasonal variations in seismic velocities within Los An-geles Basin from correlations of ambient seismic noise:Geophysical Journal International, 181, 985–996.
Minato, S., T. Matsuoka, T. Tsuji, D. Draganov, J. Hunziker,and K. Wapenaar, 2011, Seismic interferometry usingmultidimensional deconvolution and crosscorrelationfor crosswell seismic reflection data without boreholesources: Geophysics, 76, no. 1, SA19–SA34, doi: 10.1190/1.3511357.
Minato, S., T. Tsuji, S. Ohmi, and T. Matsuoka, 2012a,Monitoring seismic velocity change caused by the 2011Tohoku-oki earthquake using ambient noise records:Geophysical Research Letters, 39, L09309, doi: 10.1029/2012GL051405.
Minato, S., T. Tsuji, T. Matsuoka, and K. Obana, 2012b,Crosscorrelation of earthquake data using stationaryphase evaluation: Insight into reflection structures ofoceanic crust surface in the Nankai Trough: Interna-tional Journal of Geophysics, 8, 101545.
Mordret, A., M. Landes, N. M. Shapiro, S. C. Singh, P. Roux,and O. I. Barkved, 2013, Near-surface study at the
Valhall oil field from ambient noise surface wave tomog-raphy: Geophysical Journal International, 193, no. 3,1627–1643, doi: 10.1093/gji/ggt061.
Nakata, N., R. Snieder, T. Tsuji, K. Larner, and T. Matsuoka,2011, Shear-wave imaging from traffic noise using seis-mic interferometry by cross-coherence: Geophysics, 76,no. 6, SA97–SA106, doi: 10.1190/geo2010-0188.1.
Oye, V., A. Braathen, and U. Polom, 2013, Preparing for CO2
storage at the Longyearbyen CO2 Lab: Microseismicmonitoring of injection tests: First Break, 31, 95–101.
Park, C. B., R. D. Miller, and J. Xia, 1998, Imaging dis-persion curves of surface waves on multi-channel rec-ord: 68th Annual International Meeting, SEG, ExpandedAbstracts, 1377–1380.
Park, C. B., R. D. Miller, and J. Xia, 1999, Multichannelanalysis of surface waves (MASW): Geophysics, 64,800–808, doi: 10.1190/1.1444590.
Roux, P., K. G. Sabra, W. A. Kuperman, and A. Roux, 2005,Ambient noise cross correlation in free space: Theoreti-cal approach: The Journal of the Acoustical Society ofAmerica, 117, 79–84, doi: 10.1121/1.1830673.
Saito, M., and H. Kabasawa, 1993, Computation of reflectiv-ity and surface wave dispersion curves for layered media2. Rayleigh wave calculations: Butsuri-tansa, 46, 283–298.
Schuster, G. T., J. Yu, J. Sheng, and J. Rickett, 2004, Inter-ferometric/daylight seismic imaging: Geophysical Jour-nal International, 157, 838–852, doi: 10.1111/gji.2004.157.issue-2.
Schuster, G. T., 2009, Seismic interferometry: CambridgeUniversity Press.
Snieder, R., 2004, Extracting the Green’s function from thecorrelation of coda waves: A derivation based on sta-tionary phase: Physical Review E, 69, 46610, doi: 10.1103/PhysRevE.69.046610.
Snieder, R., K. Wapenaar, and K. Larner, 2006, Spuriousmultiples in seismic interferometry of primaries: Geo-physics, 71, no. 4, SI111–SI124, doi: 10.1190/1.2211507.
Socco, L.V., S. Foti, and D. Boiero, 2010, Surface-waveanalysis for building near-surface velocity models —
Established approaches and new perspectives: Geo-physics, 75, no. 5, 75A83–75A102, doi: 10.1190/1.3479491.
Tsuji, T., T. A. Johansen, B. O. Ruud, T. Ikeda, and T. Mat-suoka, 2012, Surface-wave analysis for identifyingunfrozen zones in subglacial sediments: Geophysics,77, no. 3, EN17–EN27, doi: 10.1190/geo2011-0222.1.
Wapenaar, K., and J. Fokkema, 2006, Green’s function rep-resentations for seismic interferometry: Geophysics,71, no. 4, SI33–SI46, doi: 10.1190/1.2213955.
White, D., 2013, Seismic characterization and time-lapseimaging during seven years of CO2 flood in the Weyburnfield, Saskatchewan, Canada: International Journal ofGreenhouse Gas Control, 16, S78–S94, doi: 10.1016/j.ijggc.2013.02.006.
Xia, J., R. D. Miller, and C. B. Park, 1999, Estimation ofnear-surface shear-wave velocity by inversion of Ray-
Yamabe, H., T. Tsuji, Y. Liang, and T. Matsuoka, 2016, In-fluence of fluid displacement patterns on seismic veloc-ity during supercritical CO2 injection: Simulation studyfor evaluation of the relationship between seismicvelocity and CO2 saturation: International Journal ofGreenhouse Gas Control, 46, 197–204, doi: 10.1016/j.ijggc.2016.01.011.
Yamanaka, H., and H. Ishida, 1996, Application of geneticalgorithms to an inversion of surface-wave dispersiondata: Bulletin of the Seismological Society of America,86, 436–444.
Zimmermann, R. W., and M. King, 1986, The effect of theextent of freezing on seismic velocities in unconsoli-dated permafrost: Geophysics, 51, 1285–1290, doi: 10.1190/1.1442181.
Takeshi Tsuji received a Ph.D. fromthe Department of Earth and Plan-etary Science, University of Tokyo.He is an associate professor at theInternational Institute for Carbon-Neutral Energy Research (I2CNER),Kyushu University, Japan. He isa lead principal investigator of CO2
storage division of I2CNER. He alsoworks for the Engineering Department of Kyushu Univer-sity. He is a member of SEG and SEGJ. His research in-terests include seismic reflection and refraction analyses,surface wave analysis, rock physics, interferometric SAR,and fluid dynamics.
Tatsunori Ikeda received a B.S.(2009) in global engineering, an M.S.(2011) in civil and earth resources en-gineering, and a Ph.D. (2014) in urbanmanagement from Kyoto University,Japan. Since 2014, he has been a post-doctoral research associate at the CO2
storage division of the InternationalInstitute for Carbon-Neutral Energy
Research (WPI-I2CNER), Kyushu University. His researchinterests include surface wave analysis considering the ef-fects of higher modes, lateral variation, attenuation, andanisotropy.
Tor Arne Johansen is a full profes-sor in the Department of Earth Sci-ence, University of Bergen, Norway,and additionally holds an adjunct pro-fessorship at the university studies inSvalbard in the Norwegian Arctic. Heis a member of EAGE and SEG. Hismain research interests include seis-mic reservoir characterization, rock
physics, CCS, and seismic profiling in the transition fromland to sea in polar regions.
Bent Ole Ruud received an M.S.(1988) in geology and applied geo-physics from the University of Osloand a Ph.D. (1995) in geophysics fromthe University of Bergen. From 1988to 1994, he worked at the Universityof Oslo and from 1994 to 2003 at theDepartment of Solid Earth Physics,University of Bergen. From 2003 to
2007, he was a senior researcher at the Centre for Inte-grated Petroleum Research (UoB). Since 2007 he has beenat the Department of Earth Science (UoB). His researchinterests include modeling of seismic wave propagation,seismic processing and inversion, and use of multi-component seismic data.
