Geologic and Reservoir Characterization and Modeling
Scott M. Frailey and James Damico Illinois State Geological Survey
Midwest Geologic Sequestration Science Conference November 8th, 2011
Acknowledgements n The Midwest Geological Sequestration Consortium is funded by the
U.S. Department of Energy through the National Energy Technology Laboratory (NETL) via the Regional Carbon Sequestration Partnership Program (contract number DE-FC26-05NT42588) and by a cost share agreement with the Illinois Department of Commerce and Economic Opportunity, Office of Coal Development through the Illinois Clean Coal Institute.
n Landmark Graphics Software Donation via University Program and Schlumberger Carbon Service for technical support and consultation
Modeling Goal
n To develop a representative reservoir model based on geology, petrophysical, and fluid properties to provide guidance to n Pilot design n Active CO2 injection operations n Long-term sequestration strategies
n Specifically n Plume shape, size, and distribution n Far-field pressure magnitude and distribution
Petrophysical Challenge: Predicting Permeability
n Permeability is a function of porosity and packing arrangement/grain size
n Cores with similar porosity can have significantly different permeability values
Depth, ft MD 6763 7045
φ, % 28.5 28.6
k, md 43.2 1440
CCS #1
Petrophysical Challenge: Permeability-Porosity Core Data
0.0001
0.001
0.01
0.1
1
10
100
1000
10000
0 5 10 15 20 25 30
Cor
e Pe
rmea
bilit
y
Core Porosity
n Porosity alone is not a good predictor of permeability
CCS #1
Petrophysical Characterization: Core φ-k Transform Based on Grain
Size n Sub-divide core
data by grain size category
n Better representation of the core data
n How to pick transform based on log response?
0.0001
0.001
0.01
0.1
1
10
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10000
0 5 10 15 20 25 30
Cor
e Pe
rmea
bilit
y
Core Porosity
CCS #1
Petrophysical Characterization: Core φ-k Transform Based on
Grain Size
n Grain size correlated to Archie’s cementation exponent “m” n Resistivity and porosity logs n In-situ brine resistivity
n “m” correlated to grain-size based φ-k transforms
Schwartz and Kimminau, 1987
Geologic Characterization: Permeability Transform based on ‘m’
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6900
7100 0.001 0.01 0.1 1 10 100 1000 10000
Dep
th
K (md)
Injection Well (CCS #1) �
Dark blue line: predicted permeability using m
Pink squares: rotary sidewall core plug permeability
Injection Well: • Worked well with laterolog • Low invasion mud used
Verification Well: • Different mud used and use of
laterolog failed • Use of induction logs improved the
match, but under-predicted high permeability values
Geologic Characterization: Net Thickness
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71000.05 0.10 0.15 0.20 0.25 0.30
Porosity
Dep
th (f
t)
8% 12%10% 14%
Gross and Net Thickness:
Porosity Cutoffs
What fraction of the gross
thickness of the Mt. Simon has storage and injectivity?
CCS #1
Geologic Characterization: Gross and Net Thickness
0 8 10 12 14 Gross Injection Well Net Thickness 1505 ft 1322 ft 1009 ft 744 ft 569 ft Gross Verification Well Net Thickness
1476 ft 1344 ft 1064 ft 782.5 ft 578 ft
Porosity Cutoff, %
Geologic Model: Objectives
n Integrate petrophysical characteristics and conceptual geologic model
n Approximate the geologic architecture for gridded reservoir simulation models
n Develop multiple models based on reasonable geologic and petrophysical interpretations
Geologic Model: Monte Carlo Method
n Random generation of petrophysical properties using probability functions
n Generate numerous models quickly, but with little regard to geologic architecture
Geologic Model: Architecture and Facies
Both systems have same proportion of high permeability facies
Structured System: black are connected across model edges
Random System: black not connected across model edges
Higher Permeability Facies
Lower Permeability Facies Guin and Ritzi, 2008, Geophys. Res. Ltrs., (L10402)
Geologic Model: Petrophysical Properties Linked to Facies
n Geostatistics well established for modeling facies’ geometry for specific geologic environments
n Important to populate the geostatistically generated facies with petrophysical properties
Geologic Model: Plurigaussian Simulation
n Grain size (m) used as a proxy for facies n Maintains hierarchical relationships
between facies
Coarse: 38.3% Coarse-Medium: 6% Medium: 6.5% Medium-Fine: 6.1% Fine: 43.2%
Geologic Model: Plurigaussian Simulation
Next facies models n geophysics and geologist
interpretation to build true facies model
n Mt. Simon outcrop study planned to improve facies interpretation
Facies (grain size) model
Reservoir Model
n Gridding n Calibration n Simulations
n Plume management n Pressure management
Reservoir Model: Vertical Gridding 5500
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Porosity
Dept
h (ft
)
CoarseMediumFineUltra Fine
Model # of Layers
hlayer
ft Coarse 6 250 Medium 28 53
Fine 108 15* Ultra Fine
322 5
n Grid Selection: n Honor geologic
architecture and facies
n Understand geologic features influencing flow
n Select grid to model these features
n Grids chosen for computational reasons: for general guidance only
* 2-10 ft layers at injection zone
Reservoir Model: Gridding
n Example: n 108 layer model n Permeability (log10) n 2 miles x 2 miles by
1400-1500 ft n Granite wash included
Reservoir Model: Pre-CO2 (Water) Calibration
n CCS#1 Water Injection Pressure Transient Test n 25 ft perforated interval (injection/falloff, step-
rate test) n 25 ft + 30 ft perforated (injection/falloff)
n Injection spinner logs infer no water injection into upper perfs
Reservoir Model: Water Pressure Transient Test
n kh 185 md n kv 2.45 md n kv/kh 0.013
(over 75 ft interval)
10-‐3 10-‐2 10-‐1 100 101
10-‐3
10-‐2
10-‐1
Delta-‐T (hr)
DP & DERIVATIVE (P
SI/S
TB/D)
UNIT S LP
E NDWBS
S TABIL
S PHE R E
PPNS TB
2009/10/01-‐2229 : O IL
Par t i a l Penet r a t i on Wel l
wel l . s t or age = . 205E-‐ 02 BBL S / PS I S k i n( mec h. ) = -‐ 1. 0738 S k i n( Gl oba l ) = 10. 499 per meabi l i t y = 170. 87 MD Per m-‐ Thi c knes s = 12815. MD-‐ FEET Kv / Kh = . 548E-‐ 02 Ef f . Thi c knes s = 81. 402 F EET P-‐ ex t r ap. = 3108. 30 PS I R( i nv ) a t 26. 47 hr = 908. F EET Smoot hi ng Coef = 0. 10, 0.
Type-‐ Cur v e Model S t a t i c -‐ Dat aPer f . I nt er v a l = 25. 0 F EET
S t at i c -‐ Dat a and Cons t ant sVol ume-‐ Fac t or = 1. 000 v ol / v olThi c knes s = 75. 00 F EETVi s c os i t y = 1. 300 CPTot a l Compr es s = . 1800E-‐ 04 1/ PS IRat e = -‐ 6100. S TB/ DS t or i v i t y = 0. 0003240 FEET/ PS IDi f f us i v i t y = 8023. F EET^2/ HRGauge Dept h = N/ A F EETPer f . Dept h = N/ A F EETDat um Dept h = N/ A FEETAna l y s i s -‐ Dat a I D: GAU001Bas ed on Gauge I D: GAU002PFA S t ar t s : 2009-‐ 09-‐ 24 19: 04: 38PFA Ends : 2009-‐ 10-‐ 06 02: 29: 04
Partial Penetration/Completion Model
Comparison of Water PTA and Transformed Permeability
n kh estimated every 0.5 ft
n kv/kh of 0.85 used every 0.5 ft for kv
n Harmonic average (series flow) used to calculate kv over 75 ft interval.
Log/ Core PTA
kh, md 182 185
kv, md 2.43 2.45
h, ft 78 75
Reservoir Simulation Pilot Design Applications
Plume Management n Location of verification
well n UIC permit specifications
Pressure Management n Equipment and hardware
n Injection equipment selection (centrifugal pump)
n Maximum pressure ratings (valves and gauges)
Plume and Pressure Management n Injection wellbore
n Perforation interval selection n Location of the packer in the injection
well
Reservoir Simulation: Plume Management Example
Single Perforated Interval: Year 1 Upper Perforated Interval: Year 2
Reservoir Simulation: Pressure Management Example
n Bottomhole injection pressure compared to fracture pressure
n Pore pressure below caprock and intermediate seals compared to capillary entry pressure
n Far-field pressure and “regulated” area of review and pressure thresholds
Conclusions
n Unique method used to transform core permeability to well log porosity
n Realistic geologic model based on facies and architecture n facies model populated with petrophysical
properties n Water injection validated geologic model of
injection zone n good agreement between core, logs and PTA
Conclusions, contd.
n Model development n Objective and data driven n Not model driven
n Model limitations and applications n Geologic and petrophysical uncertainty n Specific models not appropriate for all tasks
n Realistic expectations of reservoir models
Geologic and Reservoir Characterization and Modeling
Scott M. Frailey and James Damico Illinois State Geological Survey
Midwest Geologic Sequestration Science Conference November 8th, 2011