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Design of Experiment & Response Surface Modeling of CO 2 Sequestration in Deep Saline Aquifers

Design of Experiment & Response Surface Modeling of CO 2 Sequestration in Deep Saline Aquifers. Funding: ACS PRF ( (# 48773-DNI 8 ) & NSF ( EAR-0838250 ). Baozhong Liu, Ye Zhang yzhang9@uwyo.edu Dept. of Geology & Geophysics, University of Wyoming, Laramie, WY

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Design of Experiment & Response Surface Modeling of CO 2 Sequestration in Deep Saline Aquifers

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  1. Design of Experiment & Response Surface Modeling of CO2 Sequestration in Deep Saline Aquifers • Funding:ACS PRF ((# 48773-DNI 8) & NSF (EAR-0838250) Baozhong Liu, Ye Zhang yzhang9@uwyo.edu Dept. of Geology & Geophysics, University of Wyoming, Laramie, WY http://faculty.gg.uwyo.edu/yzhang/

  2. Motivation Deep saline aquifer Celia, M. (2006) Princeton University’s Carbon Mitigation Initiative.

  3. Motivation • In modeling CO2 sequestration in deep saline aquifers, data available for characterizing aquifer heterogeneity are limited; • A variety of stratigraphic models have been used in site assessment studies; • Sequestration in saline aquifers is a “cost center”, detailed aquifer characterization is impractical.

  4. Questions • (1) Can stratigraphic models capture the flow behaviors of the heterogeneous aquifer? • (2) How can we address question (1), when at any storage site, multiple geological and engineering parameters exhibit significant uncertainty? • (3) To answer question (2), we can attempt a parameter sensitivity analysis (SA). However: • In field to basin-scale modeling of CO2 storage, complex physical & chemical processes are involved (multiple phases, multiple components, reactions, deformations, etc.) over long simulation times (1000 years upwards), traditional SA is too expensive.

  5. Study Approach • upscaling permeability for a synthetic aquifer  multiple stratigraphic models of decreasing complexity  multiple conceptual models at a given field site; • DoE & RS modeling: conduct a parameterSA & prediction uncertainty analysis for all models, based on the same set of uncertain input parameters; • Within a full parameter space, evaluate each stratigraphic model against the parameter sensitivity & prediction uncertainty of the FHM; • Multiple simulation outcomes are evaluated, over increasingly longer times (injection, monitoring);

  6. Facies Model8 Upscaled K* Fully Heterogeneous Model (FHM); K=K(x,y,z) Formation Model 1 Upscaled K* Depositional Model 3 Upscaled K* Details on upscaling, see Zhang et al. (2011) TIPM

  7. Saline Storage Model Populate aquifer with the 4 conceptual models (aquitard is assumed homogeneous):

  8. CO2 Modeling • Eclipse 300 (GASWAT); • Injection Rate: 0.45 ~ 0.90 kg/s; Injection Duration: 40~20 years (total mass is fixed); • Monitoring: 500 years; • Boundary Condition: “open” on all sides; external aquifer on top; no-flow bottom; • Model depth: 1, 2, 3 km (below results are of the 2 km depth); • Assumptions: no fluid-rock reactions; no coupling of flow with geomechanics or heat transport;

  9. Input Parameter Varied AquG: vertical aquifer hydraulic gradient; TG: geothermal gradient; VAR: s2lnk (aquifer); SGR: Sgr (residual gas saturation) SAL : brine salinity; q: CO2 injection rate; krock: caprock permeability Residual Gas Saturation: Bounding imbibition curve is varied: Sgr

  10. DoE & RS Modeling • DoE (Plackett-Burman Screening Design): MANOVA • Identify important parameters that have statistically significant effect on a prediction outcome; • Generate a RS model of the outcome, as a proxy for reservoir simulation: • outcome = fcn (important parameters); • RS model  prediction envelopes of the outcome (without reservoir simulation);

  11. Sensitivity Analysis

  12. Prediction Envelope

  13. Prediction Envelope

  14. Maximum Brine Leakage (Cumulative)

  15. FHM Plume Migration Facies Run 1: VAR=0.1 Run 6: VAR=7.0 Depositinal Formation

  16. Gas Profiles Mobile gas Trapped gas Dissolved gas

  17. Gas Profiles Mobile gas Trapped gas Dissolved gas

  18. Conclusions • The above results are not sensitive to changing depth (not shown); • In the full parameter space tested, depositional and facies models are nearly equally accurate in predicting gas profiles, plume shape, and brine leakage of the FHM. The optimal model is the depositional model; • In the full parameter space tested, the formation model is only adequate in pressure prediction, thus it is useful for estimating brine leakage; • Accuracy of stratigraphic models is strongly influenced by the variance of lnk (VAR);

  19. Future Work • Different heterogeneity pattern & connectivity; • Different aquifer geometry; • Advanced DoE & RS designs (e.g., correlated factors; higher accuracy in RS models); • Application to field tests;

  20. Future Work 2.4 million cells

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