Sim-SEQ: A Model Comparison Study for
Geologic Carbon Storage
S. Mukhopadhyaya,, Z. Houb, L. Gosinkb, D. Baconb, C.
Doughtya, J. Lic, L. Weic, S. Gasdad, G. Baccie, R.
Govindane, J.-Q. Shie, H. Yamamotof, R. Ramanathanb,
JP Nicotg, S.A. Hosseinig J.T. Birkholzera, A.
Bonnevilleb
Presented by Curt Oldenburga
aLawrence Berkeley National Laboratory, Berkeley, CA, USAbPacific Northwest National Laboratory, Richland, WA, USAcShell (China) Innovation and R&D Centre, Beijing, ChinadCentre for Integrated Petroleum Research, Uni Research, Bergen, NorwayeImperial College London, UKfTaisei Corporation, Yokohama, JapangBureau of Economic Geology, University of Texas at Austin, Austin, USA
Acknowledgment
•DOE/NETL for providing financial support
•Susan Hovorka (BEG, University of Texas Austin) for sharing
the site characterization and observation data from the S-3 site
•All participating institutions and the modeling team members
Presentation Outline
• Model uncertainty and the need for a model comparison
study for GCS systems
• An overview of the Sim-SEQ project
• Brief Introduction to the Sim-SEQ Study Site
• Comparison of predictive models and lessons learned
• Future directions
• Vastly differing time scales
• Processes are coupled and highly nonlinear
• Heterogeneities on different scales
• Sparsity of data in field situations
• Difficult-to-measure and uncertain parameters
Wide range of predictions because of different modeling
techniques, coupling methods, approaches for multiphase
behavior, interpretations of site data
Challenges in Modeling CO2 Storage
Project Objectives
•There are two different uncertainties associated with making
predictions about flow at GCS sites
–Uncertainties related to the site or the processes (limited information
about the geology; sparse or unreliable monitoring data, etc.)
–Model uncertainties (uncertainties arising from different interpretation
of geology and monitoring data)
•Sim-SEQ addresses model uncertainties:
―If the same site characterization and injection scenario is given to
multiple modeling teams, will they produce identical prediction?
―If not, what causes the differences in predictions made by different
modeling teams?
Model Comparison
Site AModel
Team 1
Model
Team 2
Model
Team 3
Model
Team 6
Model
Team 7
Model
Team 4
Model
Team 5
To increase stakeholders’
confidence in GCS systems, we
need to understand the root
causes of model uncertainties
and, if possible, quantify these
uncertainties
This is accomplished by
engaging in a model
comparison study involving both
model-to-data and model-to-
model comparison at one or
more selected GCS field sites.
Sim-SEQ is not Code Comparison or
Benchmarking
Benchmarking exercises related to GCS problems have been
conducted in the past (Pruess et al., 2004; Class et al., 2009;
Nordbotten et al., 2012)
Sim-SEQ is Model Comparison•Model comparison evaluates modeling studies in a broad and
comprehensive sense.
•Comprises all work flow stages - interpretation of site
characterization efforts, parameter choices, model assumptions,
decisions about domain sizes and boundary conditions, etc.
•The DECOVALEX project on model comparison in geologic
disposal of nuclear wastes (Tsang et al., 2009) serves as an
analog for Sim-SEQ.
•In the works since 2009
•Actual kick-off meeting in April 2011
•After a modest start, currently 15 participating teams
•A Sim-SEQ web portal has been launched
(https://gs3.pnl.gov/simseq/wiki) - password protected site,
access to Sim-SEQ participants only
Sim-SEQ Overview
The S-3 Site
The S-3 site is patterned after the Southeast
Regional Carbon Sequestration Partnership
(SECARB) Phase III Early Test in the
southwestern part of the state of Mississippi
in the USA.
The target formation at the S-3 site is
comprised of fluvial sandstones of the
Cretaceous lower Tuscaloosa Formation at
depths of 3300 m
Denbury Onshore LLC has hosted (since
2007) the SECARB Phase II and Phase III
tests in a depleted oil and gas reservoir
under CO2 flood.
The tests are managed by the Bureau of
Economic Geology (BEG) at the University of
Texas, Austin.
Pictures: Courtesy of JP Nicot (BEG)
Modeling Challenges
The DAS area comprises fluvial
deposits of considerable
heterogeneity located in the water leg
of an active CO2-EOR field with a
strong water drive.
These features add significant
complexity when approximating the
natural system, and challenges arise
in dealing with boundary conditions.
In addition, presence of methane has
been confirmed in the brine, which
can potentially exsolve and impact
pressure buildup history and CO2
plume extent
Acknowledgment: JP Nicot (BEG)
Depth below
ground
surface
(feet)
Fault trace at
reservoir level
X & Y coordinates in feet
Reservoir is nominally
880 feet thick – this
ssurface + 80 ft.
Inje
cto
r B
HL =
(24
82
67
, 3
87
665)
OB
S #
1 B
HL =
(24
84
94
, 3
87
645)
OB
S #
2 B
HL =
(24
86
33
, 3
87
684)
Formation Top Surface
Facies 1: Standstone
(orange)
Mean perm: 359.68 mD
Variance: 3.39
Porosity: 0.27
Facies 2: Sandstone and
conglomerate (hot pink)
Mean perm: 44.25 mD
Variance: 2.21
Porosity: 0.26
Facies 3: Everything else
Mean perm: 9.07 mD
Variance: 6.63
Porosity: 0.16
Petrographic Analysis and Heterogenity
Acknowledgment: Diana
Bacon and Ramya
Ramanathan (PNNL)
Injection Data
Preliminary Conceptual Models of the
S-3 Site
•Conceptual models developed by the following teams are
included in the comparative analysis presented here (in
alphabetical order)
1. Imperial College London, UK (ICL)
2. Lawrence Berkeley National Laboratory, USA (LBNL)
3. Pacific Northwest Laboratory, USA (PNNL)
4. Shell (China) Innovation and R&D Center, China (Shell)
5. Taisei Corporation, Japan (Taisei)
6. Uni Research, Norway (UNR)
F1 F3F2
Acknowledgment: JP Nicot (BEG)
Comparison of Preliminary Model
Results
F1 F3F2
Acknowledgment: JP Nicot (BEG)
~70 m ~30 m
Comments
Qualitative model comparison illustrates that
―Model conceptualization plays a significant role in
deciding outcome
―To improve model prediction, need to include more site-
specific parameters/factors (relative permeability, entry
pressure, residual saturation, etc.) not considered
―Model refinement using observation data will likely
reduce the range of predictions
Ongoing Activities & Future Plans
Iterative model refinement utilizing observation data from the
S-3 site.Evaluation of the influence of methane on CO2 flow
History-matching and calibration of the predictive model using pressures
from the two observation wells and CO2 arrival times
Impact of far-field production and injection, and preferential flow paths
on model prediction
Quantitative model comparison and uncertainty analysis
Reactive transport modeling
Extension to other GCS sites
Integration with NRAP
Thank You
Questions?
Attributes of the Six Selected
Conceptual Models
ITEM ICL LBNL PNNL Shell Taisei URN
Software Eclipse/E300 TOUGH2/EOS7C STOMP-
CO2e
MoReS TOUGH2-
MP/ECO2N
VESA
Components Water+CO2 Water+CO2+CH4 Water+CO2+
Salt
Water+CO2+
Salt
Water+CO2+
Salt
Water+CO2
Grid Type 3-D, Rectangular ,
Uniform
3-D, Voronoi
tessellation,
Irregular
3-D,
Rectangular,
Irregular,
3-D,
Rectangular,
Irregular
3-D
Cylindrical,
Voronoi
tessellation,
Irregular
2-D, Rectangular,
Uniform
Orientation No Tilted (2o) Top
boundary-
fitted
Top
boundary-
fitted
Top
boundary-
fitted
No
Model extent 2,000 m 2,000 m 4,000 m 5,200 m 3,218 m
3,218 m
5,000 m
5,000 m
1,200 m
(radius)
610 m 610 m
Number of
layers
8 8 16 40 50 0
Number of
gridblocks
40,000 4968 44,944 182,240 223,901 40,000
LBNL
PNNL
Total Gridblocks: 44,944Total Gridblocks: 4,968
Taisei, Japan
Shell, China
Gridblocks = 223901
Connections = 887,915
(4478 x 50 layers + 1 well)
Total gridblocks: 67x68x40 = 182,240
Boundary and Initial Conditions
ICL LBNL PNNL Shell Taisei URN
Fault N/A No flow No flow No flow N/A N/A
Top/bottom
boundaries
Closed Closed Closed Closed Closed Closed
Side boundaries Constant
pressure
Constant
pressure
Constant
pressure
Semi-
analytical
aquifer model
or closed
Constant
pressure
Constant
pressure
Initial pressure ~32 MPa ~32 MPa ~32 MPa ~32 MPa ~32 MPa ~32 MPa
Initial temperature 125oC 127oC 128oC 128oC 100oC 125oC
Initial salt N/A N/A 157gpl 150 gpl 123 gpl N/A
Initial CH4 N/A Water
saturated
with dissolved
CH4
N/A N/A N/A N/A
Injected fluid Pure CO2 92%
CO2+8% CH4
Pure CO2 Pure CO2 Pure CO2 Pure CO2
Rock Properties
ITEM ICL LBNL PNNL Shell Taisei URN
Perm/poro Homogeneous
(layerwise)
Homogeneous
(layerwise)
Heterogeneou
s (facies)
Heterogeneou
s (facies)
Homogeneous
(layerwise)
Homogeneous
Source Core data (F-
2 & F-3)
Logs &
sidewall cores
( F-1)
Core data (F-
2 & F-3)
Core data (F-
2 & F-3)
Log data (F-
3)
Average value
of core data
(F-2 & F-3)
Anisotropy No Yes Yes Yes Yes No
Upscaling No Yes Yes Yes Yes No
Rel perm van
Genuchten
Corey From cores Corey van
Genuchten
Brooks-Corey
PC van
Genuchten
van
Genuchten
Brooks-Corey Core data van
Genuchten
No Pc
Sgr 0 0 0.20 0 and 0.2 0.2 0.2
(1) (2) (3)
(4) (5) (6)
Anisotropy (2:1)
Anisotropy (1:2)
Anisotropy (1:1)
Sequential indicator
GeoModel1 GeoModel2 GeoModel3
Truncation Gaussian
GeoModel4 GeoModel5 GeoModel6
Realization #1 (perm/poro
constant in each facies)
Realization #4 (perm/poro
random in each facies)
Realization #7 (perm/poro
Gaussian in each facies)
PN
NL
Permeability scale 0 (cyan)-400 mD (red)
Shell
Uncertainty Analysis
Generalized linear model (GLM) analyses are used to test the significance
of linear and interaction terms/factors for each response variable
),0(~,* 2
1
,0 NpYiid
ii
n
j
jiji
An Akaike's information criterion (AIC) based backward removal approach is
used to identify the most significant parameters.
The statistical significance of input parameters is evaluated through null
hypothesis tests
A t-statistic and a P-value are calculated for each input parameter
If the P-value is larger than the significance level of the test (e.g., 0.1), one
can accept the hypothesis that the corresponding parameter is not significant.
Factors
F-2
Breakthrough
Time, day
F-3
Breakthrough
Time, day
Max
Pressure
30d, MPa
Max
Pressure
180d, MPa
Max
Pressure
365d, MPa
RMSE
bpw2,
MPa
RMSE
bpw3,
MPa
RMSE
MPa
Perm * *** ** *
Poro ** * *** **** **
Perm:p
oro* ** **
Rzx ** * *** * **** **
Rzy ** ** *
Ryx **
Rzx:Rzy ** * *
Scheme ** * *** **** ** **** ** **
Statistical Significance of Parameter
Modeling scheme plays the most significant role
Response variables are more sensitive to the input parameters at intermediate
times
Simplified models (homogeneous and isotropic) produce the largest deviances
Highly complex models (3-D heterogeneous and anisotropic) models have more
simulation errors compared to models with intermediate levels of complexities
Comments
Results are inconclusive
―The preliminary statistical analyses are based on a
limited number of experiments (only 14) and a large
number of response variables (8).
―Need to include more parameters/factors (relative
permeability, entry pressure, residual saturation, etc.) not
considered in this study.
―More refined observation data are needed for more
conclusive results
Site Characterization and Injection Data
1. Location of the injection formation (e.g., top and bottom coordinates of the
Tuscaloosa Formation; formation thickness; fault trace)
2. Construction details and bottomhole locations of the injection (SS31F1) and
observation wells (SS31F2 and SS31F3)
3. Open hole logs from the injection and two observation wells
4. Porosity/permeability from core plugs and sidewall cores from the injection and
two observation wells
5. Porosity/permeability from other nearby wells (SS46-2 - ~1900 m from DAS;
SS29-12 - ~2,200 m from DAS; SS28-1 - ~3,450 m; and SS44-6 - ~3,950 m from
DAS)
6. Relative permeability and mercury injection test data from SS29-12
7. Petrographic analysis of the injection formation
8. Geochemical analysis of the formation brine
9. Carbon dioxide injection rate; bottomhole pressure and temperature data from the
injection well
Acknowledgment: JP Nicot, BEG
No. Organization/Institution Name of Software/Model Further Information
1. Bureau of Economic Geology, USA CMG-GEM http://www.cmgl.ca/software/gem.html
2. Bureau de Recherches Géologiques
et Minières, France
TOUGH2/Eclipse
/Petrel
http://esd.lbl.gov/research/projects/tough/software/tough2.htm;
http://www.slb.com/services/software/reseng/compositional.aspx;
http://www.slb.com/services/software/geo/petrel.aspx
3. Geological Storage Consultants,
USA
VESA Gasda et al. (2009)
4. Imperial College, UK Eclipse http://www.slb.com/services/software/reseng/compositional.aspx
5. Institute of Crustal Dynamics,
China
CCS_MULTIF Yang et al. (2011a,b), Yang et al. (2012)
6. Lawrence Berkeley National
Laboratory, USA
TOUGH2-EOS7C http://esd.lbl.gov/research/projects/tough/software/tough2.html;
Oldenburg et al. (2004)
7. Pacific Northwest National
Laboratory
STOMP-CO2E http://stomp.pnnl.gov; White et al. (2012)
8. Research Institute of Innovative
Technology for the Earth, Japan
TOUGH2-
ECO2N
http://esd.lbl.gov/research/projects/tough/software/tough2.html;
Pruess and Spycher (2007)
9. Sandia National Laboratory, USA Not available
10. UFZ, Germany OpenGeoSys http://www.ufz.de/export/data/1/19757_OGS_5_concept_V1.pdf
11. Shell, China MoReS Wei (2012)
12. Taisei Corporation, Japan TOUGH2-
MP/ECO2N
http://esd.lbl.gov/research/projects/tough/
13. Uni Research, Norway VESA Gasda et al. (2009)
14. University of Stuttgart, Germany DUMUX http://www.dumux.org
15. University of Utah, USA STOMP-CO2E http://stomp.pnnl.gov
Organization ChartPI: Sumit Mukhopadhyay (LBNL)
Good reservoir
non reservoir
non reservoir
16%6%
44
64
Rock facies based on porosity
and gamma logs
Heterogeneity From Well Log Data
Core data from F2 and F3
Poor sand
Good Sand
Observation Wells F2 and F3 Core Data
Porosity : 1.29-31.44%; mean 21.76%
Permeability: 0.01-1890 mD; mean: 2.91 mD
PNNL Transition Probability
Based Facies Model
Facies 1: Standstone
(orange)
Mean perm: 359.68 mD
Variance: 3.39
Porosity: 0.27
Facies 2: Sandstone and
conglomerate (hot pink)
Mean perm: 44.25 mD
Variance: 2.21
Porosity: 0.26
Facies 3: Everything else
Mean perm: 9.07 mD
Variance: 6.63
Porosity: 0.16
Petrographic
Analysis from F2
Sources of Model Uncertainty
www.co2crc.com.au
•Need to predict the fate of injected CO2
•Uncertainty of subsurface processes and of
their spatial/temporal scales
• Uncertainty of the subsurface geology and
of the distribution of parameters (flow, PVT,
geochemistry, etc.)
• Choices made by modelers: software to be
used, which processes, coupling of
processes, multiple length scales and grid
discretization, boundary conditions
•These choices cause a wide range in
model predictions
Gantt Chart
Year FY11 FY12 FY13 FY14
Quarter 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Task 1:
Data Review
and Site
Selection
- - -
Task 2:
Model
Development
- - -
Task 3:
Predictive
Simulations
- - -
Task 4: Model
Refinement - - -
Task 5:
Technical Team
Participation
- - -
Annual Reports - - -
LBNL Layer Properties•Layers are equal thickness
except for one thinner low-
permeability layer inferred to be
a shale baffle
•Well-log permeabilities are
scaled to well-test permeability
(multiply by 1.76)
•Arithmetic average for
horizontal permeability
•Harmonic average for vertical
permeability, times anisotropy
factor of 0.5 (literature value)
•No lateral heterogeneity,
except well column permeability
decreased to represent skin
effect (well-test analysis)
Bibliography
• Journal, multiple authors:
– Mukhopadhyay, S., Birkholzer, J.T., Nicot, J.P., and Hosseini, S.A., 2012, A model
comparison initiative for a CO2 field injection test: An introduction to Sim-SEQ.
Environmental Earth Sciences, doi: 10.1007/s12665-012-1668-1, available at:
www.springerlink.com.
Curt,
I am attaching the presentation for the IEAGHG meeting next week. There are 27 slides (the last one is just a thank you slide for your
convenience). You may delete slides 15-20 and 23-25, which will bring the total slide count to about 18. I kept those slides (9 in total) so that you have
all the details while making the presentation. You can just rush through them or put them at the end so that, if anybody has a specific question, you
can use them.
I have also added a few more slides (after slide 27) with some details, just in case you need those additional details.
Let's meet sometime today/tomorrow, after you have time to go through the slides, particularly if you need any other data/info.
Thanks for doing the presentation
Sumit