Continuous passive-seismic monitoring of CO2 geologic ...

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Continuous passive-seismic monitoring of CO2 geologic sequestration projects Biondo Biondi, Stanford University with Sjoerd de Ridder and Jason Chang, Stanford University

Transcript of Continuous passive-seismic monitoring of CO2 geologic ...

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Continuous passive-seismic monitoring of CO2 geologic sequestration projects

Biondo Biondi, Stanford University

with

Sjoerd de Ridder and Jason Chang, Stanford University

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CCS monitoring

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www.e-education.psu.edu/meteo469/node/223

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“Volumetric” monitoring by 3D seismic

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www.e-education.psu.edu/meteo469/node/223

Active sources Surface receivers

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Volumetric monitoring by passive 3D seismic

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www.e-education.psu.edu/meteo469/node/223

Surface receivers

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History of interferometric seismic imaging

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Jon Claerbout (1968)

Geophysics

Invented on Earth

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History of interferometric seismic imaging

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Stanford/NASA

soi.stanford.edu/results

Jon Claerbout (1968)

Geophysics

Invented on Earth Apllied on the Sun

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History of interferometric seismic imaging

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Stanford/NASA

soi.stanford.edu/results

Jon Claerbout (1968)

Geophysics

Shapiro et al. (2005)

Geophysics

Invented on Earth Applied on the Sun Back to Earth

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Ambient seismic noise Virtual seismic source Correlations

Seismic interferometry - Step 1 Record data at two receivers

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Ambient seismic noise Virtual seismic source Correlations

Seismic interferometry - Step 1 Record data at two receivers at the surface

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Seismic interferometry – Step 2 Cross-correlate recorded traces

Ambient seismic noise Virtual seismic source Correlations

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Ambient seismic noise Virtual seismic source

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Seismic interferometry – Step 3 Synthesize ”virtual events” – Interface waves

Correlations

Surface waves (transmission)

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Seismic interferometry – Step 2 Cross-correlate recorded traces

Ambient seismic noise Virtual seismic source Correlations

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Seismic interferometry – Step 3 Synthesize ”virtual events” – Body waves

Ambient seismic noise Virtual seismic source Correlations

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Body waves (reflections)

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2x real-time

Ambient seismic noise

Distance (km)

0 2.5 5 7.5 10

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Ambient noise (time slice) Ambient noise (time vs. distance)

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20

1

0

0

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2x real-time

Ambient seismic noise

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Ambient noise (time slices) Virtual source (time slices)

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Monitor CO2 reservoir by reflections?

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CO2 reservoir

Surface waves

Reflections

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Sleipner - Active seismic monitoring of CCS

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Andy Chadwick (2009) - www.bgs.ac.uk/staff/profiles/0130.html

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Sleipner - Monitoring by reflection images

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Andy Chadwick (2009) - www.bgs.ac.uk/staff/profiles/0130.html

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Direct detection of CO2 plumes before they reach the surface?

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CO2 reservoir

Surface waves

Reflections

CO2 plume

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Indirect detection of CO2 plumes deep in the subsurface?

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CO2 reservoir

CO2 plume Upward migrating CO2

acts as a pressure

conduit and changes

stress field close to the

surface

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Salha (Algeria) CCS project

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Allan Mathieson et al. (2010) – Energy Procedia

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Salha (Algeria) CCS project - SAR monitoring

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Allan Mathieson et al. (2010) – Energy Procedia

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``Moore’s law’’ for seismic acquisition Number of channels for one active source

23 credit: David Monke (Apache) &Tim Keho (Aramco)

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Valhall - Life Of Field Seismic array (LOFS)

24 credit: Olav Barkved (BP)

• 120 km seismic cables

• 2500 groups of 4C sensors

• 50 m receiver spacing

• 300 m cable spacing

• 15 Hz geophones, but they

record down to ≈.5 Hz

signals

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Other datasets from (semi)permanent arrays

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credit: Ali Tura (CP)

4,000 4C Fiber optic sensors

Ekofisk Long Beach 3,000 autonomous MEM sensors

credit: Dan Hollis

(Nodal Geophysical)

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Other datasets from (semi)permanent arrays

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4,000 4C Fiber optic sensors

Ekofisk Long Beach 3,000 autonomous MEM sensors

credit: Dan Hollis

(Nodal Geophysical)

credit: Ali Tura (CP)

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Other datasets from (semi)permanent arrays

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4,000 4C Fiber optic sensors

Ekofisk Long Beach 3,000 autonomous MEM sensors

credit: Dan Hollis

(Nodal Geophysical)

credit: Ali Tura (CP)

2.7 TB/day .5 TB/day

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Spectrum of ambient seismic field at Valhall

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Valhall - Virtual seismic source

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Monitor CO2 reservoir by reflections?

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CO2 reservoir

Surface waves

Reflections

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Monitor CO2 reservoir by reflections? Not yet

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CO2 reservoir

Surface waves

Reflections

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Direct detection of CO2 plumes before they reach the surface?

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CO2 reservoir

Surface waves

Reflections

CO2 plume

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1.50-1.75Hz

Identifying velocity anomalies Validation with active-data images at ≈ 80 m

60-105m

Full Waveform Inversion on active data Ambient seismic noise tomography

Landes et al, 2007 De Ridder, 2011 33

QuickTime™ and a decompressor

are needed to see this picture.

QuickTime™ and a decompressor

are needed to see this picture.

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0.50-0.75Hz

Identifying velocity anomalies Validation with active-data images at ≈ 170 m

150-195m

Full Waveform Inversion on active data Ambient seismic noise tomography

Landes et al, 2007 De Ridder, 2011 34

QuickTime™ and a decompressor

are needed to see this picture.

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Direct detection of CO2 plumes before they reach the surface? Most likely!

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CO2 reservoir

Surface waves

Reflections

CO2 plume

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Indirect detection of CO2 plumes deep in the subsurface?

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CO2 reservoir

CO2 plume Upward migrating CO2

acts as a pressure

conduit and changes

stress field close to the

surface

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Subsidence-related stress changes in subsurface

contraction

extension extension

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Gas Pocket

Barkved, 2012

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Detecting changes in stress field

credit: Barkved, BP Norge

• Shear wave splitting maps

• Polarization of fast split shear wave

and lag size

• Production related subsidence

• Input for geomechanical inversion

• Can be monitored over time

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Indirect detection of CO2 plumes deep in the subsurface? Will test on Valhall and Ekofisk

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CO2 reservoir

CO2 plume Upward migrating CO2

acts as a pressure

conduit and changes

stress field close to the

surface

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What about land data? Preliminary results from Long Beach data

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Long Beach 3,000 autonomous MEM sensors

Ambient noise (time slices)

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What about land data? Preliminary results from Long Beach data

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Ambient noise (time slices) Virtual source (time slices)

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Ongoing and future investigations

• Analyze Long Beach for time-lapse effects

• Analyze Ekofisk and Valhall data for anisotropy

• Look for reflections in all three datasets by

performing longer correlations to improve SNR

at high frequencies

• Record data on an actual CCS project

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Ongoing and future investigations

• Analyze Long Beach for time-lapse effects

• Analyze Ekofisk and Valhall data for anisotropy

• Look for reflections in all three datasets by

performing longer correlations to improve SNR

at high frequencies

• Record data on an actual CCS project

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Ackowedgements

• GCEP for financial support

• SEP affiliate companies for financial support

• Joe Dellinger and Olav Barkved at BP for data,

ideas, and help

• Dan Hollis at Nodal Geophysical and Signal

Petroleum for Long Beach data

• Ali Tura at ConocoPhillips for Ekofisk data

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