ZERT Controlled Release Experiment
Lee H. Spangler
Director, ZERT and The Energy Research InstituteMontana State University

1

Zero Emissions Research & Technology
A collaborative involving Universities and
DOE National Labs
Montana State University - Lead Institution
 Los Alamos National Laboratory
 Pacific Northwest National Laboratory
 West Virginia University
 Lawrence Berkeley National Laboratory
 National Energy Technology Laboratory Lawrence Livermore National LaboratoryMontana - A Brief Comparison
Japan
Area:
Population

377,930 km2
127,078,680

GermanyArea:357,021 km2
Population
82,282,988
0.7% of world’s coal reserves
NorwayArea:384,802 km2Population
4,660,539
3rd largest oil exporter
MontanaArea:
380,837 km2
Population
967, 440
6% of world’s coal reserves
Significant Oil & Gas
3

Atmosphere
Biosphere
Soil(Vadose & Shallow
Saturated Zones)

Near – Surface
Monitoring Zones• Atmosphere– Ultimate Integrator– Dynamic– Monitoring & Modeling• Biosphere

Caprock &Deep Overburden

Injection Zone

– dynamic– requires protection– opportunity for wide area
monitoring but indirect
methods• Soil– Integrates– dynamic• Aquifers– Integrates– Requires protection4

Motivation (2006)
The situation in 2006 when we started planning thework:• Near-surface detectors were considered highly
desirable for public assurance• They had been deployed at sequestration pilot sites• These pilot sites were well chosen and do not leak
• Thus, the near-surface detection techniques had notbeen adequately tested under realistic conditions• The primary initial purpose was detection verification5Facility Goals• Develop a site with known injection rates for testingnear surface monitoring techniques• Use this site to establish detection limits formonitoring technologies• Use this site to improve models for groundwater –vadose zone – atmospheric dispersion models• Develop a site that is accessible and available formultiple seasons / years

Scaling
10 m
1,000 m

100 m

100 m

100 m
1,000 mImagine a realistic feature that might result in leakage

We chose to mimic a fault which might be on the order of 1
km long with a surface expression of 10m – 100m in width.
A 100m horizontal well would be 10% of the first case and 1%of the second case.
Scaling by a factor of 10 – 100 is a reasonable extrapolation
Sally Benson Lee Spangler

What Are Relevant Release Rates?
• 4 Mt/year injection from 500 MW power plant• 50 years injection - Total of 200 Mt Injected• Consider maximum leakage rates discussed to mitigate
climate change– 1% over 100 years = 0.01% / year = 0.0001– 1% over 1000 years = 0.001% / year = 0.00001• 200,000,000 x 0.00001 = 2,000 Tonnes / yr
• 5.5 Tonnes / Day• This is the equivalent of about 85 idling cars

8

Injection Rate
Sally Benson Lee Spangler
100 m

10 m
1,000 m

100 m

100 m
1,000 m

Years
20

40

60

Scaled Leakage Rate (t/day)

100

10

1

0.1

0.01

Scale to 1000 m leak
1,000 kg/day: 1 tonne/day

80

Years

100
0

0.1%
0.01%
0.001%

20

40

60

80

10

Scaled Leakage Rate (t/day)

0

0.1%
1

0.01%
0.1

0.001%
0.01

0.001

We used a 0.15 Tonne / Day rate
An idling car generates about 0.04545 kg CO2 /min or 64.5 kg CO2 / day. Our injection rate isabout equal to 2.3 idling cars

100Field Test Facility

MSU Agricultural lands

Route

Experiment Site

Horizontal Well Installation
Ray Solbau, Sally Benson
70 m
2m

PackerPressuretransducer
Electric cable
Packer inflation line
CO2 delivery lines
Strength line

40 cm

PackerPacker
12 m

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9

Methods• Soil Gas Monitoring• In-situ soil gas probes• Eddy Covarience• Soil Flux chambers• Differential Absorption LIDAR• Cavity ring-down, other isotopic measurements• Water chemistry• Tracers• Hyperspectral / mutispectral imaging• Many more
13

CO2 Background is Highly Variable

Affected by sunlight,
precipitation, wind,
etc. Red line shows
diurnal variation, butthere are also shortterm and much longer
term variations
14

Large Number of Participants / Methods
47 investigators
31 instruments / sensor arrays
5 univ. 6 DOE labs, 4 companies

Investigator

Institution

Arthur Wells
Rod Diehl
Brian Strasizar

1 tower (4m)
Blimp (Apogee
Scientific) with 3 tether
line samplers
Bee hive monitoring for 2 hives
tracer with sorption tube
and pollen trap
Automated Soil CO2
4 chambers
flux system
University ofHand held hyperspectral 1 instrument
California- Santa Cruz measurements (plant
health)United States
Ground water
1 EC and temperature
Geological Survey*
monitoringprobe, Dissolved
oxygen probe, lab
analysis of water
samples
West Virginia
Water monitoring well 1 sensor
University
headspace gas sampling
1 instrument
Brookhaven National Ineleastic neutron
scattering (total soil
Laboratory*
carbon)Montana Tech*
Soil moisture, temp.
5 sensors
Chlorophyll Content
Meter , Fluorescence
Meter , LI-COR 2000 tomeasure leaf area index
Leaf Porometer tomeasure stomatal
conductance
Infrared radiometry
2 instruments(plant health)Atmospheric humidity 1 sensor eachand temperature,accumulated rainfall
Plant root imaging
1 camera
Soil conductivity
1 sensor
Handheld hyperspectral 1 instrument
measurements (plant
health)University of Montana* Microbial studies
Lab analysis

William Pickles
Eli Silver
Erin Male
Yousif Kharaka
James ThordsenGil
AmbatsSarah Beers
Henry Rauch
Lucian Wielopolski
Sudeep Mitra
Martha Apple
Xiaobing Zhou
Venkata Lakkaraju
Bablu Sharma+2 students

William Holben
Sergio Morales

Monitoring
Technology
National Energy
Atmospheric tracer
Technology Laboratory plume measurements

Number of Sensors

Large Number ofParticipants / Methods

Investigator

Institution

Lee Spangler
Laura Dobeck
Kadie GullicksonMontana State
University

Kevin Repasky (PI)Jamie Barr
Rand Swanson
Joseph Shaw (PI)
Justin Hogan
Nathan Kaufman

Julianna Fessenden+3 students

Sam Clegg
Seth HumphriesThom Rahn

Monitoring
Technology
Water content
reflectometers (soilmoisture)
Automated soil CO2
flux system

Number of Sensors
15 sensors
5 long term
chambers, 1 portable
survey chamber
6 sensors

CO2 soil gas
concentration
Montana State
Underground fiber
4 sensors
University
sensor array (CO2 soil
gas concentration)
Resonon*Flight based
1instrument
hyperspectral
imaging system
Montana State
Multi-spectral
1instrument
University
imaging system (plant
health)Meteorological
1 tower
measurements
Los Alamos National In situ (closed path) 1 instrument
Laboratory
stable carbon isotope
detection system
Flask sampling for in Lab analysis
situ isotope detection
Los Alamos National Frequency-modulated 1 instrument
Laboratory
spectroscopy (FMS)
open-air path
Los Alamos National Eddy covariance
1 towerLaboratory
Pacific Northwest
Soil CO2 flux
27 chambers
National Laboratory (steady-state)
Stanford University* Commercial cavity
1 instrument/ Picarro
ringdown real-time
Instruments*
measurements of δ13C
and CO2 in air

James Amonette
Jon Barr
Sally Benson (PI)Sam Krevor
Jean-Christophe
Perin
Ariel Esposito
Chris Rella (Picarro)
Greg Rau
Lawrence Livermore Commercial cavity
1 instrument
Ian McAlexander
National Laboratory ringdown real-time(LGR)/Los Gatos Research* measurements of δ13C
and CO2 in air
Jennifer Lewicki
Lawrence Berkeley CO2 soil gas
8 sensors
National Laboratory concentration
CO2 atmospheric
2 sensorsconcentration
Chamber soil CO2
1 instrument
flux measurements
Meteorological
1 towerFlux Chamber
J.L. Lewicki

Eddy covariance net CO2 flux monitoringd-1 for 1 month)

An eddy covariance (EC) station was
deployed ~30 m NW of the releasewell in 2006, 2007, and 2008.

In 2008 (0.3 t CO2leakage signal was detected in raw
EC CO2 flux (Fc) data. Ecosystem
CO2 fluxes were modeled andremoved from Fc to improve signal
detection in residual flux (Fcr) data.

J. Lewicki (LBNL)
A least-squares inversion of
measured residual CO2
fluxes and corresponding
modeled footprint functions
during the 2008 releasemodeled the distribution of
surface CO2 fluxes, allowing
us to locate and quantify (towithin 7%) the leakage
signal.

18

Shallow CO2 Flow Modeling (1)C. Oldenburg (LBNL)TOUGH2/EOS7CA was used to address the origin of patchy emissions at the
ZERT shallow-release experiment.
High-flux regions correlatewith packer locations.

A three-dimensional grid (3D) was
developed that captures thechanges in elevation of the pipe.

3D longitudinal grid with 52,569 gridblocks (4779
gridblocks per XY-plane).
.

19

Shallow CO2 Flow Modeling (2)C. Oldenburg (LBNL)

Results suggest that packer locations influence emission patterns.
qCO2 = 100 kg CO2/day
Three-dimensional results of XgCO2
at t = 3 days showing patchy
emission pattern.

Base Case (6 zones)

Case 1 (23 zones)t = 1.5 hr

t = 1.5 hr

t = 12 hr

t = 12 hr• Patches are correlated with packer locations and highelevation regions in each zone in the soil material.
• With more packers (i.e., more zones), there are still early
breakthroughs but overall emission is less patchy.
• Therefore, simulations support the hypothesis that alongpipe flow of CO2 upwards within each zone leads to aneffective point-source release that creates a persistentpatchy emission.

20

Hyperspectral Imaging

True Color

Analyzed Image

Spectral Imaging System:
Imaging Spectrometer,
Data Logger/System Control,
IMU/GPS/Communications

Hyperspectral Imaging Unsupervised Classification
Kevin Repasky

Multi-spectral imaging for detecting CO2 leaks
J. Shaw

Multispectral imagers used to detect plant stresscaused by CO2 leaking from underground.

Time-series plot showing that the CO2-affected
plant health decays faster over time than thecontrol region. This plot shows Normalized
Difference Vegetation Index (NDVI), foundfrom NIR and red reflectances as(NIR-red)/(NIR+red)

Tethered balloon multispectral imaging at ZERTJ. Shaw

Compact, ultra-low-cost multispectral imager designed for
deployment on tethered balloon.

Studying the vegetation response to simulated leakage ofsequestered CO2 using spectral vegetation indices
Ecological
Informatics 5(2010) 379–389

Montana Tech
Venkata Ramana
Lakkaraju,
Xiaobing Zhou,
Martha E. Apple,
Al Cunningham,
Laura M. Dobeck,
Kadie Gullickson,
Lee H. Spangler

Geochemical Monitoring

USGS, LBNL, EPRI, WVU, MSU - Environ Earth Sci (2010) 60:273–284
Liange Zheng, John A. Apps, Nicolas Spycher, Jens T. Birkholzer, Yousif
K. Kharaka, James Thordsen, Sarah R. Beers, William N. Herkelrath,
Evangelos Kakouros, Robert C. Trautz, Henry W. Rauch Kadie S.
Gullickson

Geochemical
Monitoring

Environ Earth Sci (2010) 60:273–284
Int. J. Greenhouse Gas Control (2011)

USGS, LBNL, EPRI, WVU, MSU

Liange Zheng, John A. Apps, Nicolas Spycher, Jens T.
Birkholzer, Yousif K. Kharaka, James Thordsen, Sarah
R. Beers, William N. Herkelrath, Evangelos Kakouros,
Robert C. Trautz(1) calcite dissolution could be the primaryprocess buffering pH and releasing Ca+2
in groundwater,(2) the increase in the concentrations ofmajor cations and trace metals except Fe
could be explained by Ca+2-driven
exchange reactions,(3) the release of anions from adsorption
sites due to competing adsorption ofbicarbonate could explain theconcentration trends of most anions, and(4) the dissolution of reactive Fe minerals(such as fougerite) could explain theincrease in total Fe concentration.

Atmospheric monitoring of a perfluorocarbon tracer atthe 2009 ZERT Center experiment
NETL
Natalie Pekney , Arthur
Wells , J. Rodney Diehl,
Matthew McNeil, Natalie
Lesko, James Armstrong,
Robert Ference
Atmospheric Environment 47(2012) 124e132‹#›

Atmospheric monitoring of a perfluorocarbon tracer

Tower, 1 m

fL/L
‹#›

Tower, 2 m

Balloon, 10 m

Tower, 3 m

Tower, 4 m

Balloon, 20 m

Balloon, 40 m

Inline Fiber Sensor
K. Repasky
Laser

Transmission
Detector
Gap

FC/PC to SMF PBG
FC/PCSegment 1
Transmission Coupler
Detector

SMF

Gap
PBG

Segment 2

SMF

PBG

Segment 3

The inline fiber sensor uses a series of segmented
photonic bandgap (PBG) fiber in series to for a
inline fiber sensor array.
Each segment is addressed using time of flight of
the laser pulse.
CO2 diffuses into the PBG fiber to allow
spectroscopic measurements of CO2 concentration.

Challenge: PBG fiber is larger diameter than
SMF and conventional splicing collapses
hollow core

The PBG fiber allows
interaction of the laser lightand CO2 in the hollow core.
0.4
0.35
Transmission (V)In-line Fiber
Beam-Splitter

0.3
0.25
0.2
0.15
0.1
0.05

Initial un-normalized CO2 measurements made 0
using one segment of the inline fiber sensor. 0.00

0.05

0.10

0.15

0.20

Differential Absorption Lidar
K. Repasky

CRDS instrument
mounted on a golfcart and driven back– and – forth acrossthe site

Cavity Ring Down
Spectrometer Survey

32

Process Based Method

33

Resistivity

What We Have Learned• Many near surface methods are quantitative but– Diurnal, seasonal, annual variations in ecosystem background flux
affect detection limits– Appropriate area integrated, mass balance is a challenge• Nearly all methods could detect 0.15 tonnes/day release at ZERTAtmospheric signals drop rapidly away from the ground surface• Isotopes & tracers have lower detection limits than straight CO2
flux or concentration• Scaling, 6 tonnes per day would be detectable over an area 40
times as large• Surface expression was “patchy” – 6 areas of ~5m radius• Natural analogs also seem to have “patchy” surface expression• By comparing multiple controlled release sites we see thatdifferent ecosystems respond somewhat differently
35Why is the Surface Expression “Patchy”

Less
Permeable
Soil

If the horizontal permeability is significantly less than the verticalpermeability, CO2 will spread laterally until it hits a lower
permeability vertical path. It can then desiccate that path creating a“chimney”

If CO2 Escapes the Reservoir
k3

k2

k1

Many processes could prevent itfrom reaching the surface
including
1. Trapping under a secondary seal
2. Geochemical conversion of CO2
3. Dissolution

If it does reach the surface:
1. The surface flux will not necessarilybe the same as the flux leaving thereservoir
2. The surface expression could besome distance from the storage
reservoir
37

Monitoring – A Multi-Step Process• Initial Detection (Finding anomalies in a large area.)– Wide Area – Hyperspectral Imaging, Atmospheric tomography– Moderate Area – Lidar, Fiber sensors, Resistivity• Confirmation (Is anomaly due to elevated CO2 flux?)– CO2 flux and / or concentration measurements, watermeasurements• Attribution (Is elevated flux due to leakage?)– Isotopic measurements– Process based measurements (relationships between multiple
gases, Romanack)• Mapping and Quantification– Flux chamber– Concentration measurements in a survey mode

• Impact Measurement – Dependent on the receptor
38What Is the Monitoring Purpose?
• Climate change mitigation?
– 1% over 1000 yrs – climate models?

• Retention in the reservoir?
– Subsurface techniques typically do not measureproperties directly proportional to concentration /quantity• Overall storage security?
• HSE, Resource protection (USDW)?

If this is theprimary focus,
this could– Measure to ensure levels are below impact levels reduce need forwide – area
Public assurance?
monitoring.

•• Verification and accounting?

– Mass flow meters only accurate to ~1%39How We Have Learned
Natural Analogs• Mammoth Mountain• Laacher See• Latera• Soda Springs, ID• Crystal Geyser, UT• MoreHow analogous is theanalog?
Flow through significant
overburden
Fluxes may be much higher
than leaky engineered
systemControlled Releases• ASGARD (Nottingham)• ZERT (Montana State)• Australia• Norway• MoreSource term known
Ability to establish
detection limits
Relatively little overburden

40

Acknowledgement
U.S. Department of Energy
This presentation was prepared with the support of the U.S. Department of Energy(DOE), under Award No. DE-FC26-04NT42262 and DE-FE0000397.. However, any
opinions, findings, conclusions, or recommendations expressed herein are those of
the author(s) and do not necessarily reflect the views of the DOE.
Disclaimer:"This report was prepared as an account of work sponsored by an agency of the United States
Government. Neither the United States Government nor any agency thereof, nor any of theiremployees, makes any warranty, express or implied, or assumes any legal liability or responsibility
for the accuracy, completeness, or usefulness of any information, apparatus, product, or process
disclosed, or represents that its use would not infringe privately owned rights. Reference herein toany specific commercial product, process, or service by trade name, trademark, manufacturer, orotherwise does not necessarily constitute or imply its endorsement, recommendation, or favoringby the United States Government or any agency thereof. The views and opinions of authors
expressed herein do not necessarily state or reflect those of the United States Government or any
agency thereof."42