1. CO2-PENS A CO2 SEQUESTRATION SYSTEM MODEL SUPPORTING RISK-BASED DECISIONS
PHILIP H. STAUFFER
HARI S. VISWANTHAN
RAJESH J. PAWAR
MARC L. KLASKY
GEORGE D. GUTHRIE

2. Talk Outline PART I Part II Description of CO2-PENS system model
Example problem: Reservoir Injectivity with reduction of complexity
problem description
logic for reduced complexity
results

3. Talk Outline PART I Part II Description of CO2-PENS system model
Example problem: Reservoir Injectivity with reduction of complexity
logic for reduced complexity
codes used
results

4. Part I Description of CO2-PENS system model

5. Performance assessment framework for geologic sequestration
From the power plant
Into the Ground
Back toward the Atmosphere
Entire CO2 sequestration analysis
System analysis yields meaningful site comparisons
Provides consistent output for Quality assurance/Quality control

6. Linking Process-Level Modules to a System Model
(probabilistic)
CO2 release
fluid flow
geochemical
reactions
process-level models

7. Big problem: collaborators needed for process modules.
Princeton – analytical well bore leakage
MIT – surface pipeline model
Atmospheric scientists
Economists
Modular design means flexibility
CO2 multiphase reactive transport codes: FEHM, FLOTRAN, TOUGH. etc.
Analytical solutions

8. Risk-Based Decisions Predictions use probabilistic approach
Sampling of multidimensional solution spaces
Reduced complexity: abstraction, lookup tables
Generate distributions from experiment, modeling and expert opinion

9. Use existing knowledge:
Theory, experiment, lessons learned
Industry data (Kinder-Morgan), Weyburn, Sleipner
Performance assessment experience (Yucca Mountain, WIPP, Oil/gas, Los Alamos Environmental)
Economic experts
Risk theory experts

10. Part II Example Problem Reservoir Injectivity

11. Reduced complexity reservoir injection module
Analytical single fluid approximation run as a dynamic link library from GoldSim
2-D radial, multiphase finite volume calculations used to ‘tune’ the analytical solution

12. Analytical Approximation of Injection
single fluid
no relative permeability model
uses reservoir PT CO2 viscosity and density
infinite radius with pressure fixed at Pini
runs very quickly as a dynamic link library
can be coded in FORTRAN, C++ etc.
Reference C.S. Matthews and D.G. Russel, (1967). Pressure Buildup and Flow Tests in Wells, Society of Petroleum Engineers, Monograph Vol 1, New York.

13. FEHM 2-D Radial Simulation of Injection and Plume Growth
Control volume finite element method
Multiphase heat and mass transfer
Relative permeability (H20-CO2)
All constitutive relationships are in the code (e.g., density, viscosity, enthalpy)

14. Comparison of FEHM with published results
5 km x 30m deep radial grid
Nordbotten et. al, (2005)
FEHM

15. Example Assumptions

16. Example Problem Description
30 m deep section
No flow top and bottom boundaries
Far-field at background pressure
CO2 coming from a 1 GW power plant for 50 years (300 Mt CO2)

17. Relative Permeability Function

18. Two cases (Nordbotten et. al, 2005)
Cold + Shallow 1 km
Hot + Deep 3 km
Pressure 10
30 MPa
Temperature 35
155 C
Max injection pressure 15
45 MPa
Water density
999
929 kg/m3
CO2 density
714
479 kg/m3
Water viscosity
7.2e-4
1.8e-4 Pa s
CO2 viscosity
5.8e-5
4.0e-5 Pa s

19. Linear Effective Stress Relationship minimum principle stress = 0
Linear Effective Stress Relationship minimum principle stress = 0.65 lithostatic
Gives
maximum injection pressure
Reservoir background pressure
Two cases

20. Points were simulated in FEHM to span a range of permeability and porosity
0.13
0.15
0.17
+ stdv
5e-14 m2
mean
1e-14 m2
Permeability
- stdv
5e-15 m2
- stdv
mean
+stdv

21. FEHM simulations versus analytical solution
These plots yield are used to “tune” the injector code to recreate FEHM behavior in GoldSim

22. Computational time Goldsim calling the tuned analytical solution
1000 realizations in 6.5 minutes.
Includes passing all variables through the framework, generating output and storing results.
FEHM simulations, 700 nodes
10+ minutes each
(some issues with iterative solver used for CO2 EOS, we are implementing a lookup table approach)

23. Economic Risk
Cold + Shallow 1 km
Hot + Deep 3 km

24. Health/Environmental Risk
Cold + Shallow 1 km
Hot + Deep 3 km

25. Engineering Risk Leakage from the Reservoir
Percent per Year
Percent Total

26. Conclusions
Tuned analytical solution is much faster than running a reservoir solver
reduced complexity will be vital for performing risk analysis
Integrated approach shows interactions between different types of data and outcomes

27. THANK YOU
Please contact me if you are interested in collaborating on process level modules or the system model