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