Surface and Subsurface MVA...Surface and Subsurface MVA FWP FE-10-0001 Samuel M. Clegg, Kristy...
Transcript of Surface and Subsurface MVA...Surface and Subsurface MVA FWP FE-10-0001 Samuel M. Clegg, Kristy...
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Surface and Subsurface MVAFWP FE-10-0001
Samuel M. Clegg, Kristy Nowak-Lovato, Julianna Fessenden-Rahn, Ron Martinez, Robert Currier, Steve Obrey, and Lianjie Huang
Los Alamos National Laboratory
U.S. Department of EnergyNational Energy Technology Laboratory
Carbon Storage R&D Project Review MeetingDeveloping the Technologies and
Infrastructure for CCSAugust 12-14, 2014
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Presentation Outline
• Project Overview– Goals and Objectives
• Benefit to the Program• Technical Status
– Surface MVA– Subsurface MVA
• Accomplishments to Date• Summary
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Project Overview: Goals and Objectives
• Surface MVA – Frequency Modulated Spectroscopy– Quantitatively identify CO2, H2S and CH4 seepage from geologic
sequestration sites– Distinguish anthropogenic CO2 from natural CO2 emissions
• CO2 carbon stable isotope measurements• H2S sulfur and CH4 carbon stable isotope measurements
– Real-time remote and in situ CO2, H2S and CH4 monitoring– Integrated into Single Instrument
• Subsurface MVA – Advanced Seismic Imaging– Quantify reservoir geophysical properties changes Using Joint
Waveform Inversion of Time-Lapse Seismic Data– Design optimal, cost-effective surveys for time-lapse seismic
monitoring3
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Benefit to the Program • Support industry’s ability to predict CO2 storage capacity in geologic
formations to within ±30 percent. – Advanced Seismic Reservoir Imaging
• Develop and validate technologies to ensure 99% storage permanence. – FMS CO2, H2S, and CH4 Monitoring– Advanced Seismic Reservoir Imaging
• Develop technologies to improve reservoir storage efficiency while ensuring containment effectiveness.
– FMS CO2, H2S, and CH4 Monitoring– Advanced Seismic Reservoir Imaging
• Develop Best Practice Manuals for monitoring, verification, accounting, and assessment; site screening, selection and initial characterization; public outreach; well management activities; and risk analysis and simulation.
– FMS CO2, H2S, and CH4 Monitoring– Advanced Seismic Reservoir Imaging
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MVA Field Experiments
• 2008 - 2014 Field Experiments– Mammoth Springs, CA– Valles Caldera, NM– Sevietta Long Term Ecological
Research, NM– Farmington, NM– Soda Springs, UT – LANL Juniper-Pinion Field Site– ZERT, MSU, Bozeman, MT
- Controlled CO2 Flow & Release Rate
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LANL MVA Program• Frequency Modulated
Spectroscopy– In situ– Remote– LIDAR– CO2, CH4, H2S (isotopes)
• Flask Collects, Mass Spectroscopy
• Water Stable Isotope Analysis
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• Detect Seepage of CO2, CH4, H2S at sequestration sites
• Isotopic Signatures for source identification
• Frequency Modulated Spectroscopy– 100x to 1000x more sensitive than
absorption spectroscopy
• Generally, the Atmosphere Contains– 98.9% 12C16O2
– 1.1% 13C16O2
• Calibration Gases Prepared In House– Available vendors were too expensive and
took too long
CO2
CH4
H2S
Stable Isotope Detection
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Frequency Modulated SpectroscopyFrequency Modulation Spectroscopy
Absorption Spectroscopy Maximum Line Strengths (HITRAN)
12C16O2 = 1.83x10-23 12CH4 = 1.00 x10-21 H232S = 1.3x10-22
13C16O2 = 2.10x10-25 13CH4 = 1.59x10-23 H234S = 1.8x10-24
From G.C. Bjorklund Optics Letters, 5, 15, 1980
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• Why 1570 – 1680nm range?– Telecom Electronics
(1550nm)– Absorption Cross Section
for Remote (hundreds of meters)
– No spectral interferences.• H2O or CO
• Why 1604 – 1609nm range?– 13C16O2 Peaks between
12C16O2 Sub-Bandheads.– 12C16O2 Peaks ~10x 13C16O2
– Multiple species detection with same hardware
CO2 Absorption Spectrum
Wavelength (µm)
1.6050 1.6055 1.6060 1.6065 1.6070
Nor
mal
ized
Inte
nsity
(arb
. uni
ts)
12C16O213C16O2
Frequency Modulated Spectroscopy
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Forward-Backward FMS Systems Model
• The Voigt profile shown on the right as black squares was used to generate simulated FMS signal as a function of carrier frequency (shown as black squares in the left-hand plot). The theoretical equation for the FMS signal was then fit to that simulated FMS signal. • The resulting fit to the simulated FMS signal is shown as a red line in the left-hand plot. • The Voigt line shape corresponding to the best-fit parameters determined during that regression is then shown as a red line on the right. It accurately reproduces the original Voigt feature. • The agreement is excellent in both forward and backwards fitting. For this calculation, M=0.1 and ωm =0.1.
Voigt profileFMS Profile
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In Situ FMS Instrument
Collect Samplesfrom Field
8m MultipassWhite Cell
LICORTotal CO2Exhaust
Flask
Pump
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In Situ Observations
Historical TrendsBackground > -15 o/oo
Seepage < -15o/oo
Mass Spec 13CO2 (o/oo)
-60 -40 -20 0
FMS
13
CO
2 (o / oo
)
-60
-40
-20
020082009 Background2009 Over Source2010 Background2010 Over Source2011 Background2011 Over Source2011 1m From Source2011 >1m From Source2012 Along Source1:1 Line10% Relative Accuracy20% Relative Accuracy15o/oo
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Inte
nsity
Frequency
Carbon DioxideCarbon Dioxide Calibration
Incr
easi
ng C
once
ntra
tion
R² = 0.991
0
1
2
3
4
5
6
7
8
0 250 500 750 1000 1250
Volta
ge
Concentration (ppm)
12CO2 Calibration
1605.47…
R² = 0.9928
0
1
2
3
4
5
6
0 250 500 750 1000 1250
Volta
ge
Concentration ppm
13CO2 Calibration
1606.99…
Estimated Detection Limit12CO2 and 13CO2 < 1 ppb
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Inte
nsity
Frequency
Methane
Methane Calibration
Incr
easi
ng C
once
ntra
tion
0123456789
10
50 150 350 600 800 1,000
Volta
ge
Concentration ppm
12CH4 Calibration 1651 cm-1
Estimated Detection Limit12CH4 and 13CH4 < 1 ppb
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Inte
nsity
Frequency
Hydrogen Sulfide
Hydrogen Sulfide Calibration
Incr
easi
ng C
once
ntra
tion
R² = 0.9844
1500
1550
1600
1650
1700
1750
1800
0 20 40 60 80 100 120
mV
Concentration (ppm)
H232S Calibration
1578 cm-1
Estimated Detection LimitH2
32S < 1 ppbH2
34S < TBD
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Integrate into LIDAR Instrument
Add CH4 and H2S detection to CO2 LIDAR instrument
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Quantify Reservoir Changes Using Joint Waveform Inversion of Time-Lapse Seismic Data
• Motivation– Accurate quantification of
reservoir changes can be used as a non-invasive tool for tracking CO2 plume, and is crucial for long-term MVA for geologic carbon storage.
• Objective– Study the field applicability
of our recently developed algorithm for joint waveform inversion of time-lapse seismic data.
• Field Data: Time-lapse walkaway vertical seismic profiling (VSP) surveys at the SACROC EOR field– As part of efforts of SWP
Phase II to acquire the data– A baseline VSP data were
acquired before CO2injection
– A repeat VSP data were acquired about six months after CO2 injection
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Quantify Reservoir Changes Using Joint Waveform Inversion of Time-Lapse Seismic Data
100 shot points along the blue line were used to acquire VSP data recorded at 15 levels of geophone in the monitoring well.
Joint waveform inversion result of time-lapse walkaway VSP data from SACROC reveals a low velocity zone near the CO2injection locations.
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Quantify Reservoir Changes Using Joint Waveform Inversion of Time-Lapse Seismic Data
• Joint waveform inversion algorithms can quantify changes of geophysical properties within CO2reservoirs.
• Algorithms can quantifying reservoir changes with either: – time-lapse surface seismic data or – time-lapse VSP data
• Our algorithms can be combined with optimal survey designs for cost-effective monitoring. – We have developed a seismic-wave sensitivity
analysis method for optimal designs of time-lapse seismic surveys.
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Accomplishments to Date• Frequency Modulated Spectroscopy
– Developed CH4 and H2S detection methods– Integrated CH4 and H2S detection into existing in situ
CO2 FMS instrument– Developed CH4 and H2S calibration curves– Developed Forward-Backward System Model
• Advanced Seismic Imaging– Quantify reservoir geophysical properties changes Using
Joint Waveform Inversion of Time-Lapse Seismic Data– Design optimal, cost-effective surveys for time-lapse
seismic monitoring20
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Summary• Key Findings
– FMS• Stable isotopes are sensitive signatures of seepage from carbon sequestration
and EOR sites.• Detection of these stable isotope signatures can be integrated into the same
instrument.– Advanced Seismic Imaging
• Joint Waveform Inversion of Time-Lapse Seismic Data can Quantify changes in reservoir geophysical properties
• Design optimal, cost-effective surveys for time-lapse seismic monitoring
• Lessons Learned– Field experiment are critical tests to validate the instruments and algorithms
developed under this program• Future Plans
– Integrate the CH4 and H2S detection methods into the LIDAR instrument and to field test the integrated instrument.
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