1. Si-Yong Lee Model development & Aneth site example

2. What is a model? A model is a simplified representation of reality or any device that represents a system.

3. Why model? - Predictive application (predicting the consequences of a proposed action) - Interpretive application (understanding system dynamics) - Generic application (analyzing processes in generic/hypothetical settings)

4. What types of models? Conceptual Model: Qualitative description of system Mathematical Model: Mathematical description of system - Analytical solution - Numerical solution Physical Model: e.g. core flooding experiment

5. Modeling Protocol Define Problem Conceptual model Mathematical model Computation Comparison with field data Results Model Calibration Model Redesign

6. Define the problems/objectives Site selection - storage capacity - Injectivity - Plume distribution (AOR) Monitoring design Uncertainty/Risk assessment

7. Data Collection Hydrologic data (local & regional) Geologic data (e.g., stratigraphy, formation tops, faults/fractures, tectonic information, and seismic events) Geophysical data (e.g., well logs, seismic survey) Rock properties (por, perm, relative perm, Pc, bulk density, Young’s modulus, Poisson’s ratio, mineralogy, etc) Fluid properties (salinity, pH, density, viscosity, mutual solubility, brine chemistry, isotope, etc) Well information (location, vertical/horizontal, perforation interval, injection/production history, bottom hole pressure, etc)

8. Conceptual Model Cross-bedded aeolian Navajo Ss (outcrop in Devil’s canyon, UT) Conceptual model of the cross-bedded bedform 3D cross-bedded bedform Grain flow (dune) Wind ripple (interdune)

9. Grid building An optimally-sized model domain should : - Encompass all the major flow units (formations of interest – injection zone, overlying and underlying formations) - Include the injection, monitoring, and any production wells - Lie within the extent of pressure response area - Be tractable computationally

10. Grid resolution (dx, dy, dz) Grid resolution vs. computational efficiency Should include heterogeneity, well configuration, and sufficient accuracy in the changes of results (pressure & saturation). Coarsening of model grid further from the injection well (no more than 1.5 times the previous nodal spacing). Grid coarsening could create numerical dispersion.

11. Assigning property parameters - Single value in a cell (REV, scale issue) - Sparse data in space (especially horizontal direction) - Heterogeneity - Property upscaling

12. Heterogeneity and Aniostropy Heterogeneity : Variations through space Aniosotropy : Variations with the direction of measurement at any given point

13. ( Heterogeneity and Aniostropy (x 1,z 1 ) (x 2,z 2 ) kxkx kzkz Homogeneous, Isotropic Homogeneous, Anisotropic Heterogeneous, Isotropic Heterogeneous, Anisotropic

14. Approaches to generate heterogeneity Deterministic approach: parameter values are known with certainty (single solution) Stochastic approach: uncertainty in parameter values (ranges in solution) Actual Geology Layer Cake Model Stochastic/Geostat. Model

15. Stochastic Approaches Continuous Heterogeneity Gaussian model (mean, variance, and variogram) Fractal model Discrete Heterogeneity Facies model with indicator geostatistics Depositional simulation Process imitation (mathematically-based equations) Structure imitation (probabilistically-based) Mixed Heterogeneity (continuous + discrete)

16. Core description (LLNL site)

17. (TPROGS1) TProGS Realization

18. (TPROGS1) TProGS Realization (largest connected channel body)

19. Spatial Covariance of LnK

20. SGS Realization (GAUSS1)

21. SGS Realization (largest connected body) (GAUSS1)

22. TProGS vs. SGS TProGSSGS RFDiscrete(e.g. facies unit)Continuous Spatial Process MarkovianGaussian Variability Measure Transition ProbabilityCovariance Advantage - Asymmetry - Juxtapositional tendency - Sharp contact - Easy application - Simple and fast algorithm Disadvantage - Relatively more uncertain in x, y than z direction - Poor Connectivity of extreme values (Maximum entropy)

23. Geologic Model Development in Aneth site - Data Acquisition - Petrophysical Properties Estimation Estimation of porosity Porosity & Permeability Relationship - Geologic Model Development

24. Data Acquisition -Core plug analyses (porosity, density, and permeability) - Geophysical well log images - Stratigraphic formation tops data - Well information - Injection/production history

28. Navajo Kayenta Wingate Chinle Dechelly Organ Rock Hermosa Ismay Gothic Desert Creek Entrada

29. Petrophysical Properties Estimation Formation No. of Samples Porosity (  ) Permeability (mD) MeanMedian Std. Dev. MeanMedianStd. Dev. Ismay100.050.020.060.470.040.78 Gothic Shale10.009 00.012 0 Desert Creek810.090.10.075.120.3118.92

30. Ambient Porosity vs. Neutron-Density Porosity

31. Upscaled Porosity Logs

32. Porosity Field (n=9,170,238; dx=dy=100m, dz=1m; nz=1,644)

33. Upscaled Porosity Field (n=227,950; dx=dy=100m; nz=41)

34. Porosity vs. Permeability

35. Permeability Field (n=227,950; dx=dy=100m; nz=41)

36. Questions ?