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

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

Part I Description of CO2-PENS system model

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

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

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

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

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

Part II Example Problem Reservoir Injectivity

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

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.

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)

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

Example Assumptions

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)

Relative Permeability Function

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

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

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

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

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)

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

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

Engineering Risk Leakage from the Reservoir Percent per Year Percent Total

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

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