Risk Assessment on CO2 Leakage
at CO2 Storage Sites
Katherine Romanak

Gulf Coast Carbon Center
Bureau of Economic Geology,
The University of Texas at Austin,

CCS Technical Workshop
January 16, 2019

Tokyo, Japan1

Talk Outline• Using risk assessment to optimize monitoring methods: an ALPMIapproach• The #1 risk to CCS projects NOT addressed by risk assessments• Technical pproaches to minimizing this unaddressed risk
• Application to offshore sites• Offshore initiative in the USA- GoMCARB partnership and Offshoreworkshops.

Risk is Addressed Before the Project Begins
1. Site CharacterizationPrimary means ofprotection. High level ofassurance required forpermitting

2. Risk AssessmentAided by modeling,
identifies potential
unwanted outcomes

3. Project Design- Design
injection to further
minimize perceived risk

Near-surface
Deep subsurface 4. Monitoring Plan
Verification Monitoring
Assurance Monitoring
Does what happened
No unwanted outcomes
conform to predictions?
3Linking Risk Assessment and Monitoring
Risk Assessment

Process is cloudy or obscured Select monitoring systems

EngineersK-12 B (CO2CARE)

Hovorka, 2017; Hovorka et al, 2014

Experts

Regulators

Process of designing andselecting monitoring canbe complex, conductedwithout documented
process, non-linear andtherefore difficult to
duplicate or justify

Chadwick BGS

Onuma andOhkawa, 2009

Romanak et al., 2012

Proposed “ALPMI” Method for Linking• Matching monitoring to risk via forward modeling using an ALPMI
process
Assessment of Low Probability Material Impact (ALPMI)• Part 1: Describing material impact quantitatively• Part 2: Sensitivity of monitoring strategy to material impact• Examples of optimizing leakage detection• Monitoring tool selection that is reproducible and transparent.
• Makes clear why different monitoring is selected for different sites
and for different business and stakeholder settings
Susan Hovorka

ALPMI Workflow
Risk assessment method
as usual
Quantify risks to definematerial impact

Explicitly model
unacceptable outcomes
showing leakage cases.

Specify magnitude,duration, location, rate ofmaterial impact

Model material impact
scenarios•••

Avoid subjective terms like safe and effective.
E.g. : Specify mass of leakage at identified horizon or
magnitude of seismicity.
Specify certainty with which assurance is neededALPMI uses models differently than
the typical history matching theexpected performanceThis method down selects to
Identify signals in the earth system that indicate orconsider only signals that
preferably precede material impact
may indicate material impact
is occurring or may occur.
Select monitoring tools that can detect
Approaches like those normallythese signals at required sensitivity
seismic survey design should bedeployed for all modeling tools
This activity as traditionally conducted.
Include all the expected components, such as
attribution, updating as needed, feedback , etc.

Forward modeling tool response is essential todeveloping the expected negative finding: “Nomaterial impact was detected by a systemthat could detect this impact.”
Deploy tools and collecteand analyze data

Only via this ALPMI process cana finding that the material
impact did not occur be robustly
documented

Report if material impact
did/did not occur

Susan Hovorka

Material impact examples (random)• Loss of CO2 at a rate greater than 10,000 tones per year for aperiod of more than 10 years @ 80% confidence• >5% probability of earthquake > magnitude 4 within 100 years• Pressure trend that will exceed calculation mechanical stability
prior to project completion• Plume migration such that location of saturation of >5% porevolume CO2 at stabilization is within a footprint (shown on a map)

Susan Hovorka

ALPMI part 2: Assess sensitivity of monitoring
strategy to material impact
Essential to forward model the impact
1. Create material impact scenarios
e.g. for CO2 leakage or change in pore pressure thatwould increase seismic risk
2. Evaluate sensitivity of instruments, spacing,frequency of data collection, other statisticalmeasures against scenarios.

Susan Hovorka

Susan Hovorka

Example of optimizing leakage detection:
above zone monitoring for leakage detectionPressure or chemistry?

Susan Hovorka

Sensitivity analysis for leakage detection
time in models
Detecting pressure signal

Detecting geochemical signal

Behni Bollhassani, UT MS thesis

Groundwater Monitoring Example•Monitoring Network
Efficiency -35 years after
leakage to surface

Unit: wells/km2
MN1: 0.322
MN2: 0.124
MN3: 0.173
MN4: 0.223
MN5: 0.223
MN6: 0.371
MN7: 0.371
MN8: 0.866
MN9: 0.742

Yang et al, 2015

Value of Information: Leakage
Assessments in the Near-Surface• Cost and effort intensive• Poor spatial coverage• Yang, 2015 - Poor detection coverage in groundwater• Controlled releases indicate unpredictable surface expression• Need to automate• High noise• Daily, seasonal variability• Difficult to define what is leakage and what is naturalvariability

Status Quo Thinking on Environmental
Monitoring• Measure “baseline” CO2 for 1 year before
project starts to document seasonalvariability.
• Monitor CO2 during project and compareto baseline.
• Significant increase from baseline during
a project could signal a leak

The Problem with
Baselines
Soil gas CO2baselines are
shifting upward

The Problem with
Baselines
Groundwater
CO2 baselines
are shiftingupward

The Problem with
Baselines

Surface seawater CO2 level near Japan

Seawater CO2baselines are
shifting upward
Source data by Japan Meteorological Agency
Courtesy of Jun Kita, RITEThe #1 risk to CCS projects NOT addressed byrisk assessments• False positives for leakage from baselinedependent environmental monitoringmethods• The risk of false positives is much greaterthan the risk of actual leakage.

False Positive Leakage Assessments• Landowner leakage claim
near the IEAGHG
Weyburn-Midale CO2
Monitoring and Storage
project• Saskatchewan Canada19

Source Attribution of Anomalous Signal isCritical• BEG’s experience in attribution: 2
blind anomalies:– Kerr Claim at Weyburn– Cranfield anomaly• Very difficult• Fast accurate attribution is CRITICAL
for– Public acceptance– Project protection– Stakeholder protection– The way we are doing it with“baselines” and “background sites will
not be successful
Dixon and Romanak,2015.

“Background Reference” site?

BackgroundReference Site
Beaubien et al., 2013

Complexity of CO2
Concentration Variations– Large datasets over long time
periods– Complex data analysis– Lack of real-time answer– Unclear trigger points– Difficult stakeholder
communication– Rising soil respiration rates
due to global warming willcause false positives forleakage.
Dave Jones, British Geological Survey

Determining What is “Anomalous”• What constitutes an “anomaly (e.g.
a potential leakage signal)?
• What parameters should be used toindicate leakage?
• When is action required (e.g.
thresholds, trigger points) ?
• What action should be taken?

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Developing ratio-based methods• Process-based approach• Define processes based on
stoichiometry of reactions• Based on 4 simple gases• (CO2, CH4, O2, N2)• Clear trigger point• Respiration line

• More immediate answer• Less data collection• No need to measure weather
parameters• Simple to explain to stakeholders

Romanak et al., GRL, 2012
Romanak et al., IJGGC, 2014
Dixon & Romanak, IJGGC, 2015

Process-Based Gas Ratio - 1
O2 vs. CO2
 Indicates natural processes
that affect CO2 concentrations
 Distinguishes among
respiration, CH4 oxidation anddissolution
 Gives an initial assessment ofleakage

LeakageProcess-Based Gas Ratio - 2
CO2 vs. N2
 Identifies whether gas has
migrated from depth.
 Indicates whether CO2 isbeing added through leakage
or lost through dissolution.

LeakageMotivation• Rigorous testing of methodology is critical• Ensure high level of stakeholder confidence• Must understand signals in a variety ofenvironments• New learnings from monitoring operationalprojects• Reassess the Cranfield surface gas anomaly• Refine and expand process-based method• formerly only aerobic processes• Now including anaerobic processes• Ramifications for identifying industrial signals

Cranfield Anomaly• Cranfield CO2-EOR site, Mississippi, USA• US DOE SECARB RCSP site
• 1950’s plugged and abandoned well
• An un-remmediated “mud pit”
• 1124 m2 gravel pad• 13 multi-depth gas sampling stations as deepas 3 meters• Near-surface soil gas anomaly• 43% CO2, 45% CH4

Process-based assessment
Systematic change
towards leakage
signal along thetransect frombackground toanomaly

Ambiguous Isotopic Information

Whiticar, 1999

Ambiguous Isotopic Information

Whiticar, 1999

Overburden Characterization

Summary of Data
• Process-based ratios were consistent with a leakage signal• Methane isotopes of the anomaly that matched the reservoir• Stable carbon and hydrogen isotopes that suggested migrationfrom oil and gas reservoir (ambiguous)• Location near a historic well.

Surprise• Modern 14C signature of anomalyProcess-Based Signature Anomaly
anomaly• Anaerobic• Near equal portions of CO2 and CH4• Acetoclastic methanogenesis : CH3COOH → CH4 + CO2• Denitrification N2O + 2H+ + 2e- → N2 + H2O• Nitrogen is included in a process-based assessment

Published Data on Industrial Spill• Conrad et al. 1999
• Degradation of an aviation gasoline
leaked from storage tanks in 1970s• Alameda Point, San Francisco Bay.
• Soil gas samples.
• CH4 26.4%, and CO2 21%.
• O2 0 to 21%• 14C from 5.4 to 90.4 % pMC.

• Degradation by acetate fermentation

Process-basedAnalysis
Conrad Data
Acetoclastic methanogenesis :CH3COOH → CH4 + CO2A

BCD

Denitrification?

Denitrification?

Revisiting theCranfieldAnomaly• Similar shift

Laboratory Confirmation

14C

Modified from Coleman, 1994

Bio-oceanographic Source Attribution
Onshore:Process-Based Method

Offshore:
Bio-Oceanographic Method

Katherine Romanak, BEG, USA
Romanak et al., 2012, 2014
Dixon and Romanak, 2015

Jun Kita, MERI, Japan
Uchimoto et al., 2017

Tomakomai Environmental Monitoring

Jun Kita, 2017, 2nd International
Workshop on Offshore Geologic CO2 storage

1 year Tomakomai Data
compared to10 Years of Osaka Bay Data
10000

Tomakomai Data
pCO2 (μatm)

1000

100

1010.0

50.0

100.0

150.0

DO (%)

200.0

250.0

300.0

Optimal Attribution Methods• Simple, accurate, reliable method• Cost-effective method• Standardized method with a global
trigger points• Sure protocol for responding to claims• Minimize false outcomes• Maximize stakeholder trust• Method that can be easily andeconomically implemented at anindustrial scale.
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Main Overarching Points• Combining an ALPMI process with risk assessment provides for more
purposeful monitoring plans targeted to potential impacts.
• ALPMI provides a clear definition for project success.
• Baseline CO2 concentrations in soil, groundwater, and marine environments
are shifting upward due to climate change.
• Current methods of attribution which rely on baseline concentrations will
result in false leakage claims.
• Sound attribution tools are needed to avoid false positives.
• The #1 risk to projects is not leakage but the shutdown of projects due to false
positives for leakage.
• The Cranfield Anomaly required reassessment and the resulting learnings
have grown our capabilities for attribution.
• Bio-oceanographic method may be improved by reducing scatter from salinity
and temperature differences

Thank You
Katherine Romanak
Gulf Coast Carbon Center
Bureau of Economic Geology
The University of Texas at Austin
katherine.romanak@beg.utexas.edu
http://www.beg.utexas.edu/gccc/

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