Monitoring of CO2 geological storage based on the passive surface waves

International Journal of Mining Science and Technology 24 (2014) 707–711

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International Journal of Mining Science and Technology journal homepage: www.elsevier.com/locate/ijmst

Monitoring of CO2 geological storage based on the passive surface waves Dai Kaoshan a,b,⇑, Li Xiaofeng a, Song Xuehang c, Chen Gen c, Pan Yongdong c, Huang Zhenhua d a

State Key Laboratory of Disaster Reduction in Civil Engineering and College of Civil Engineering, Tongji University, Shanghai 200092, China State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China c School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China d Department of Engineering Technology, University of North Texas, Denton 76207, USA b

a r t i c l e

i n f o

Article history: Received 14 July 2013 Received in revised form 15 February 2014 Accepted 17 April 2014 Available online 17 August 2014 Keywords: Microtremor CO2 storage Passive surface wave Site characterization Feasibility study

a b s t r a c t Carbon dioxide (CO2) capture and geological storage (CCS) is one of promising technologies for greenhouse gas effect mitigation. Many geotechnical challenges remain during carbon dioxide storage field practices, among which effectively detecting CO2 from deep underground is one of engineering problems. This paper reviews monitoring techniques currently used during CO2 injection and storage. A method developed based on measuring seismic microtremors is of main interest. This method was first successfully used to characterize a site in this paper. To explore its feasibility in CO2 storage monitoring, numerical simulations were conducted to investigate detectable changes in elastic wave signatures due to injection and geological storage of CO2. It is found that, although it is effective for shallow earth profile estimation, the surface wave velocity is not sensitive to the CO2 layer physical parameter variations, especially for a thin CO2 geological storage layer in a deep underground reservoir. Ó 2014 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

1. Introduction

2. Seismic monitoring techniques for CO2 storage

Carbon dioxide (CO2) capture and storage (CCS), as one of promising technologies for greenhouse gas mitigation, usually involves capturing and separating CO2 from point sources, transporting captured CO2 to target sites, and then injecting into a geologic formation for storage [1]. Various storage technologies have been tested globally, including those used in field experiments such as the Sleipner project, the Otway Basin project, the Weyburn project, and tests implemented at Nagaoka, Japan, Ketzin, German, and Decatur, US [2–7]. For proper management of a CO2 storage reservoir, reliable monitoring techniques are required to verify whether CO2 remains safely trapped underground. A variety of strategies were proposed, including remote sensing, geophysical, geochemical, and microbial methods [8–13]. This paper reviews related methods with a focus on seismicity-based monitoring, introduces an application case of the microtremor based on seismic site characterization, and investigates the feasibility of detecting changes in elastic wave signatures due to injection and geological storage of CO2 through analyzing microtremor.

As one of geophysical testing tools, seismic monitoring is based on the observation that seismic wave velocities change as CO2 substitutes media in rock pore spaces. Wang et al. found that the compressional wave (P-wave) velocity (Vp) and the shear wave (S-wave) velocity (Vs) decrease 10.9% and 9.5% at maximum, respectively, when a carbonate rock is flooded with CO2 [14]. A series of lab measurements during CO2 flooding into sandstone also indicate detectable elastic wave velocity changes, although it is not dramatic as CO2 being injected into a methane gas reservoir [15–19]. Numerical works through seismic modeling and geochemical simulating conducted by Kumar et al. verified the effectiveness of time-lapse seismic monitoring for CO2 injection [20]. The investigation at the Ketzin site proved the feasibility of using amplitude-related attributes and time-shift seismic measurements to track changes during CO2 injection [21]. Multiphase flow simulations and forward modeling performed by Bergmann et al. indicate that time-lapse alterations of geoelectric and seismic reservoir response were observable [22]. Various field seismic monitoring programs have been implemented in CCS projects [7,23–25]. Reservoir elastic property changes caused by CO2 injection are the basics for most seismicbased techniques. If the changes are insignificant such as that of the Otway Basin Pilot Project scenario, where the depleted gas field is used for CO2 storage, time-lapse seismic methods may not work

⇑ Corresponding author. Tel.: +86 21 65981033. E-mail address: [email protected] (K. Dai).

http://dx.doi.org/10.1016/j.ijmst.2014.07.007 2095-2686/Ó 2014 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

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K. Dai et al. / International Journal of Mining Science and Technology 24 (2014) 707–711

5m (a) Test site

5m

(b) Sensor array

Fig. 1. Microtremor field measurements.

well [19]. The microtremor survey method (MSM), suitable for subsurface profiling, has found its applications in many geotechnical problems. This method listens to the natural seismic signals from human activities and natural events, and analyzes these signals to obtain the shear-velocity profile for the earth at a scale of meters to kilometers. Due to the simple instrumentation requirement and nonintrusive feature, it will be plausible if the MSM can be used for CO2 monitoring. However, the application feasibility of the MSM should be discussed for different scale geotechnical problems.

3. Microtremor measurements for site characterization The surface wave method has been used in engineering projects to estimate the shear wave velocity (Vs) profile of the earth. The active surface wave method, due to its cost and environmental impact, has certain limitations. The passive surface wave method is based on analyzing ambient noise recordings, generated by human activities or natural events. Generally, two steps are followed to estimate the Vs profile of a site: (1) the dispersion curves of surface waves are derived from the ambient noise array measurements; (2) a subsurface Vs profile is back-calculated from the dispersion curves. Two methods are often used to calculate dispersion curves, the frequency–wave number (F–K) method and the spatial auto-correlation (SPAC) method [26–30]. To investigate the effectiveness of the MSM for site characterization, a field test was performed. Nine receivers were deployed in the vertices of three triangles and one was placed at the center of the triangle, shown in Fig. 1. These ten sensors were 4 Hz geophones. The recording time was 30 s with 1000 samples per second. Twelve recording sets were obtained and each had 10 channels of vertical ground microtremor signals, as shown in Fig. 2.

0

z1,0.0 2 4 6 8 10

z2,0.0 2 4 6 8 10

z3,0.0 2 4 6 8 10

z4,0.0 2 4 6 8 10

z5,0.0 2 4 6 8 10

Each of the twelve passive records was processed by using the SPAC method. The image of dispersion curves was stacked, shown in Fig. 3a. Based on the dispersion image intensity, a dispersion curve was extracted (the dot point curve in Fig. 3b). The shear wave velocity profile was then estimated based on the genetic algorithm (GA), shown in Fig. 3b. Please note that, in Fig. 3b, the piecewise blue line is the initial shear wave velocity model in the GA inversion and the red line is the shear wave velocity profile final result. This field testing demonstrates the efficiency of the microtremor method for site survey. The test provides the shear wave velocity profile for a shallow geotechnical setting (about 50 m). Since the CO2 geological storage is often much deeper, a feasibility study is essential to investigate the sensitivity of the microtremor method for deep subsurface profiling such as CO2 geological storage monitoring.

4. Sensitivity study of the microtremor method for CO2 geological storage monitoring Seismic monitoring techniques used in CCS projects in publications primarily rely on detectable changes of the P-wave velocity, although some researchers studied S-wave responses [31]. It is found that long period seismic waves collected on the ground surface contain deep geological information [32]. Array measurements of passive low frequency microtremors were used in sedimentary exploration as deep as 1 km [33]. Since CO2 geological storage can be at this depth, microtremor collection and analysis is probably an effective approach for CO2 monitoring. To investigate the effect of CO2 storage on the surface wave velocity, a basalt reservoir model in the publication of Khatiwada et al. was used [34]. The multi-layered geological model was schematically shown in Fig. 4. Density (q, g/cm3), P-wave velocity (Vp, km/s), S-wave velocity (Vs, km/s) and thickness (d, m) of each layer listed in Fig. 4 are directly from the published data [34]. The target layer for geological storage of CO2 is h = 992 m deep under the ground surface. A method of reflection and transmission coefficients was used for determining dispersion curves of the Rayleigh wave [35–37]. An infinite thick layer was assumed to lay under the CO2 storage with the same physical parameters of the deepest layer listed in Fig. 4. The first five modes (Mode 1–5) of the Rayleigh wave were calculated and typical dispersion curves are shown in Fig. 5. Through manipulating one parameter among q, Vp, Vs, d, and h of the CO2 storage layer each time, the change of surface wave phase velocity was examined. Based on previous studies, the parameter variation is limited to 10% increase of its original

z6,0.0 2 4 6 8 10

z7,0.0 2 4 6 8 10

3000 6000 9000 12000 15000 18000 21000 24000 27000 30000

Fig. 2. Microtremor recordings.

z8,0.0 2 4 6 8 10

z9,0.0 2 4 6 8 10

z10,0.0 2 4 6 8 10

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K. Dai et al. / International Journal of Mining Science and Technology 24 (2014) 707–711 Velocity (m/s)

0

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139

195

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363

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200

400

600

800

1000 1200

(8.5,122.2)

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(17.5,191.7)

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(26.7,228.5)

28 12 16

(33.3,426.0) (39.7,560.7)

42 (48.0,467.7)

20 58 24 70

28

(704.9)

(a) Dispersion curve image

(b) Dispersion curve and shear wave velocity profile

Fig. 3. Shear wave velocity profile calculation.

h0=0

Ground surface

ρ = 1.8; V p = 1.2; Vs = 0.74; d = 80 ρ = 2.1; V p = 1.7; Vs = 1.13; d = 104

ρ = 2.8; V p = 4.8; Vs = 2.75; d = 25

ρ = 2.1; V p = 2.4; Vs = 1.28; d = 29

ρ = 2.8; V p = 5.09; Vs = 2.8; d = 49 ρ = 2.7; V p = 5.42; Vs = 3.07; d = 36

ρ = 2.3; V p = 2.4; Vs = 1.28; d = 14 ρ = 2.3; V p = 3.78; Vs = 2.13; d = 19

ρ = 2.7; V p = 5.62; Vs = 3.2; d = 70

ρ = 2.7; V p = 5.79; Vs = 3.29; d = 38 ρ = 2.1; V p = 4.5; Vs = 2.43; d = 69

ρ = 2.7; V p = 5.79; Vs = 3.29; d = 9

ρ = 2.7; V p = 5.7; Vs = 3.18; d = 268 ρ = 2.2; V p = 4.11; Vs = 2.19; d = 10 ρ = 2.7; V p = 5.68; Vs = 3.17; d = 172 hc=992

CO2 storage layer ρ = 2.7; V p = 5.54; Vs = 3.1; d = ∞

ρ = 2.3; V p = 4.26; Vs = 2.4; d = 41

ht = ∞

Deep underground (infinite earth)

Fig. 4. Schematic diagram of the CO2 storage geological site (q, g/cm3; Vp, km/s; Vs, km/s; and d, m) [34].

value at the maximum, as shown in Fig. 4, except for the thickness d, which is set up to twice bigger than the original value [14–18]. The bury depth of the CO2 layer was set to be 90% and 95% of the original one (992 m) by condensing the thickness of each layer above the CO2 storage one with the same decrease ratio. The detailed inputs are listed in Table 1. Analysis results are shown in Fig. 6, where the Rayleigh velocity (VR) amplitude difference A or B of each mode is determined by using Eqs. (1) and (2).

Phase velocity (m/s)

3000

Mode 1 Mode 2

2500

Mode 3 Mode 4

2000

A ¼ ðV CaseI  V Original Þ  100%=V Original R R R

ð1Þ

B ¼ ðV CaseII  V Original Þ  100%=V Original R R R

ð2Þ

From numerical simulation results, it is observed that the fundamental mode of Rayleigh waves is the most sensitive to physical parameter changes of the CO2 storage layer for almost all cases, except for the bury depth (h), where the wave velocity for the second mode changes more. For the same mode, the bury depth change has the most significant effects on the phase velocity variation. The Vs variation of the CO2 layer causes more visible Rayleigh wave velocity changes compared to those induced by Vp or q variations. The Rayleigh wave velocity increases with the increase of Vp, Vs, q and bury depth (h) of the CO2 storage layer, while it decreases as

Mode 5 1500

Table 1 Physical parameters for the CO2 storage layer.

1000 500

0

10

20 30 Frequency (Hz)

40

50

Fig. 5. Dispersion curves of the first five modes (Mode 1–5) of the earth layer model.

Item

q (g/cm3)

VP (km/s)

VS (km/s)

d (m)

h (m)

Original Case I Case II

2.30 2.42 2.53

4.26 4.47 4.69

2.40 2.52 2.64

41.0 61.5 82.0

992.0 942.4 892.8

K. Dai et al. / International Journal of Mining Science and Technology 24 (2014) 707–711

0.10 0.05 0

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Zoom-in in (f) 10 5

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(c) Vs

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Frequency (Hz) (d) Layer thickness

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(b) Vp

(a) Density

-1.0

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-1.2

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Phase velocity change (%)

0.05

0.15

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Phase velocity change (%)

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A B

Phase velocity change (%)

0.15

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Phase velocity change (%)

Phase velocity change (%)

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710

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Frequency (Hz) (e) Bury depth (zoom-out figure)

15 10

M3

M4

M5

M2 M1

5

0

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Fig. 6. Rayleigh wave velocity changes of the first five modes (M1–M5) at different CO2 layer parameter variations.

the thickness (d) of CO2 storage layer increases. Most detectable velocity changes of the first five modes occur within 10 Hz, which confirms that deep underground surveys mainly reply on low frequency seismic waves [33]. The maximum phase velocity change is 15.67% at 2.21 Hz when the bury depth of CO2 storage reservoir decrease 10% (Dh = 99.2 m, bury depth h = 892.8 m). However, for most cases, the Rayleigh wave velocity does not have dramatic changes. This indicates that the surface wave velocity is not sensitive enough to be used for monitoring physical property changes induced by CO2 injection in deep underground reservoirs similar to this case. Although it is uneasy to detect CO2 depletion in a deep reservoir by comparing the shear wave velocity profile shift, seismic data, especially the Rayleigh waves, have found their applications in many engineering fields, including the mining and energy industry [38,39]. 5. Conclusions CO2 geological storage is a means to mitigate greenhouse effects. However, efficient monitoring tools are required during injection and long-term storage for risk management. Time-lapse surface seismic monitoring has been effectively used in several CCS projects. This technique usually costs much and requires active sources. Microtremors collected through an array of seismometers on the ground surface have been analyzed by some researchers to map shallow earth structures. A field test was performed for site characterization demonstration and the shear wave velocity profile at the depth of 50 m was obtained. A sensitivity study was implemented to investigate the feasibility of monitoring the Rayleigh wave velocity changes caused by CO2 flooding into a deep underground reservoir through microtremor measurement. Numerical experiments were performed based on a layered basalt reservoir model. It is concluded that changes of the surface wave phase velocity induced by physical parameter variations of the CO2 geological storage layer is insignificant to be detected for most cases. For a reservoir similar to the case in this study with a thin layer buried deep in the earth, it is a challenge to monitor CO2 geological storage through comparing the shear wave velocity profile shift based on the analysis of microtremor data collected on the ground surface with conventional sensing technologies.

Acknowledgments The authors would like to acknowledge the financial supports from the State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining and Technology (No. SKLGDUEK1002) and the Fundamental Research Funds for the Central Government Supported Universities of Tongji University, China (No. 0270219037). Technical supports from the Geogiga Technology Corporation are appreciated. The views, opinions, findings and conclusions reflected in this publication are the responsibility of the authors only and do not represent the policy or position of any agency.

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