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CO2-PENS A CO2 SEQUESTRATION SYSTEM MODEL SUPPORTING RISK-BASED DECISIONS

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. Talk Outline. PART I Description of CO2-PENS system model Part II

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CO2-PENS A CO2 SEQUESTRATION SYSTEM MODEL SUPPORTING RISK-BASED DECISIONS

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  1. CO2-PENSA 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 • Description of CO2-PENS system model • Part II • Example problem: Reservoir Injectivity with reduction of complexity • problem description • logic for reduced complexity • results

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

  4. Part IDescription 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 system model (probabilistic) CO2 release fluid flow geochemical reactions process-levelmodels

  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 IIExample ProblemReservoir 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)

  19. Linear Effective Stress Relationshipminimum 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 Porosity 0.13 0.15 0.17 + stdv 5e-14 m2 mean 1e-14 m2 Permeability - stdv 5e-15 m2 mean - stdv +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 RiskLeakage 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

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