Si-Yong Lee Model development & Aneth site example.
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Transcript of Si-Yong Lee Model development & Aneth site example.
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What is a model?
A model is a simplified representation of realityor any device that represents a system.
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Why model?
- Predictive application (predicting the consequences of a proposed action)
- Interpretive application (understanding system dynamics)
- Generic application (analyzing processes in generic/hypothetical settings)
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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
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Modeling Protocol
Define Problem
Conceptual model
Mathematical model
Computation
Comparison with field data
Results
Model Calibration
Model Redesign
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Define the problems/objectives
• Site selection
- storage capacity
- Injectivity
- Plume distribution (AOR)
• Monitoring design
• Uncertainty/Risk assessment
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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)
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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)
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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
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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.
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Assigning property parameters
- Single value in a cell (REV, scale issue)
- Sparse data in space (especially horizontal direction)
- Heterogeneity
- Property upscaling
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Heterogeneity and Aniostropy
Heterogeneity : Variations through space
Aniosotropy : Variations with the direction of measurement at any given point
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(
Heterogeneity and Aniostropy
(x1,z1)
(x2,z2)
kx
kz
Homogeneous, Isotropic Homogeneous, Anisotropic
Heterogeneous, Isotropic Heterogeneous, Anisotropic
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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
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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)
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x y
z
Core description (LLNL site)
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(TPROGS1)
TProGS Realization
x y
z
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(TPROGS1)
TProGS Realization(largest connected channel body)
x y
z
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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)
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SGS Realization
(GAUSS1)
x y
z
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SGS Realization(largest connected body)
(GAUSS1)
x y
z
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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)
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Geologic Model Development in Aneth site
- Data Acquisition
- Petrophysical Properties Estimation
Estimation of porosity
Porosity & Permeability Relationship
- Geologic Model Development
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Data Acquisition
- Core plug analyses
(porosity, density, and permeability)
- Geophysical well log images
- Stratigraphic formation tops data
- Well information
- Injection/production history
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Navajo
Kayenta
Wingate
Chinle
Dechelly
Organ Rock
Hermosa
Ismay
Gothic
Desert Creek
Entrada
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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
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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
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Upscaled Porosity Logs
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Porosity Field(n=9,170,238; dx=dy=100m, dz=1m; nz=1,644)
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Upscaled Porosity Field(n=227,950; dx=dy=100m; nz=41)
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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
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Permeability Field(n=227,950; dx=dy=100m; nz=41)
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Questions ?