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What is a model?
A model is a simplified representation of realityor any device that represents a system.
Why model?
- Predictive application (predicting the consequences of a proposed action)
- Interpretive application (understanding system dynamics)
- Generic application (analyzing processes in generic/hypothetical settings)
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
Modeling Protocol
Define Problem
Conceptual model
Mathematical model
Computation
Comparison with field data
Results
Model Calibration
Model Redesign
Define the problems/objectives
• Site selection
- storage capacity
- Injectivity
- Plume distribution (AOR)
• Monitoring design
• Uncertainty/Risk assessment
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)
Conceptual Model
Cross-bedded aeolian Navajo Ss(outcrop in Devil’s canyon, UT)
Conceptual modelof the cross-bedded bedform
3D cross-bedded bedform
Grain flow (dune)Wind ripple (interdune)
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
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.
Assigning property parameters
- Single value in a cell (REV, scale issue)
- Sparse data in space (especially horizontal direction)
- Heterogeneity
- Property upscaling
Heterogeneity and Aniostropy
Heterogeneity : Variations through space
Aniosotropy : Variations with the direction of measurement at any given point
(
Heterogeneity and Aniostropy
(x1,z1)
(x2,z2)
kx
kz
Homogeneous, Isotropic Homogeneous, Anisotropic
Heterogeneous, Isotropic Heterogeneous, Anisotropic
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
Stochastic Approaches
• Continuous HeterogeneityGaussian model (mean, variance, and variogram)Fractal model
• Discrete HeterogeneityFacies model with indicator geostatisticsDepositional simulation
Process imitation (mathematically-based equations)Structure imitation (probabilistically-based)
• Mixed Heterogeneity (continuous + discrete)
x y
z
Core description (LLNL site)
(TPROGS1)
TProGS Realization
x y
z
(TPROGS1)
TProGS Realization(largest connected channel body)
x y
z
Spatial Covariance of LnK
Lag(m)
Covariance of LnK
0. 20. 40. 60. 80. 100. 120.
0.0
5.0
10.0
15.0
20.0
25.0
Covariance of Ln K
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00
0.0
5.0
10.0
15.0
20.0
25.0
Lag(m)
SGS Realization
(GAUSS1)
x y
z
SGS Realization(largest connected body)
(GAUSS1)
x y
z
TProGS vs. SGS
TProGS SGS
RF Discrete(e.g. facies unit) Continuous
SpatialProcess Markovian Gaussian
VariabilityMeasure Transition Probability Covariance
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)
Geologic Model Development in Aneth site
- Data Acquisition
- Petrophysical Properties Estimation
Estimation of porosity
Porosity & Permeability Relationship
- Geologic Model Development
Data Acquisition
- Core plug analyses
(porosity, density, and permeability)
- Geophysical well log images
- Stratigraphic formation tops data
- Well information
- Injection/production history
Navajo
Kayenta
Wingate
Chinle
Dechelly
Organ Rock
Hermosa
Ismay
Gothic
Desert Creek
Entrada
Petrophysical Properties Estimation
FormationNo. of
Samples
Porosity () Permeability (mD)
Mean MedianStd. Dev.
Mean Median Std. Dev.
Ismay 10 0.05 0.02 0.06 0.47 0.04 0.78
Gothic Shale 1 0.009 0.009 0 0.012 0.012 0
Desert Creek 81 0.09 0.1 0.07 5.12 0.31 18.92
Texaco Aneth H-117
5380
5400
5420
5440
5460
5480
5500
5520
5540
0 5 10 15 20 25 30
Porosity (%)
Dep
th (
ft)
Ambient Porosity vs. Neutron-Density Porosity
Upscaled Porosity Logs
Porosity Field(n=9,170,238; dx=dy=100m, dz=1m; nz=1,644)
Upscaled Porosity Field(n=227,950; dx=dy=100m; nz=41)
y = 0.0253e0.2824x
R2 = 0.6626
y = 0.0504e0.1655x
R2 = 0.3485
1.E-02
1.E-01
1.E+00
1.E+01
1.E+02
1.E+03
0 5 10 15 20 25 30 35
porosity (%)
k (m
D)
Desert Creek
Gothic
Ismay
Expon. (Desert Creek)
Expon. (Ismay)
Porosity vs. Permeability
Permeability Field(n=227,950; dx=dy=100m; nz=41)
Questions ?