characterization and simulation of the panoma field
TRANSCRIPT
Characterization and Simulation of the Panoma Field (Wolfcampian);
a Tight, Thin-Bedded Carbonate Reservoir System Southwest Kansas
Martin K. Dubois1, Alan P. Byrnes1, Shane C. Seals2, Randy Offenberger2, Louis P. Goldstein2, Geoffrey C. Bohling1, John H. Doveton1, and Timothy R. Carr1
1Kansas Geological Survey, 2 Pioneer Natural Resources USA, Inc.
Acknowledge support from Hugoton Asset Management Project industry partners:Pioneer Natural Resources USA, Inc.Anadarko Petroleum CorporationBP America Production CompanyConocoPhillips CompanyCimarex Energy Co.E.O.G. Resources Inc.OXY USA, Inc.W. B. Osborn
Presentation Outline
Overview, unique problems and lithofacies (Marty)
Petrophysical properties and relationships (Alan)
3D cellular model (Shane)
Initial simulations (Randy)
What’s next
Setting
Giant stratigraphic trap(s)Kansas Panoma 2.8 TCF Kansas Hugoton 24 TCF
Kansas Hugoton EUR 35-38 TCF(Olson, etal, 1996)Panoma EUR ???
Challenges and Key Points
Challenges:Automate & upscaleData volume (5200 sq miles, 2600 producers, 10,000+ wells)
Direct measurements of Sw by logs is problematic (must use property-based OGIP)
Facies representation in model is critical
Free water level varies and not documented Automate Automate
Volumetric OGIPAutomate
Some key points:1. Thinly layered reservoir, moderate to low-
crossflow between zones (pressure data indicates differential depletion)
2. Matrix properties drive the system and thin high perm layers may control flow
And one more challenge:Material balance GIP is problematic due to lack good pressure data by zone.
SIP for commingled production is available, but this represents the lowest possible SIP of the most permeable of the commingled zones.
Why Model These Mature Reservoirs?Panoma SIP vs Cum Gas
(1968-2002)
0
50
100
150
200
1 2 3Cum Gas (TCF)
Avg
. SIP
(psi
g)
(Discovered 1958)
SIP is actually pressure of highest perm zone in commingled production
Hugoton SIP vs Cum Gas(1968-2002)
0
100
200
300
400
10 15 20 25 30
Cum Gas (TCF)
Avg
SIP
(psi
g)Goals:
1. Functional comprehensive geologic and engineering models for simulation and reservoir management
2. Resolve zonal differential depletion questions
3. Resolve question of continuity between two reservoir systems that are regulated separately
Seven Sequences, Eight Lithofacies
Gas production from upper seven marine-nonmarine sequences of Council Grove
• Depth (top) 2400-2800’• 300 feet below Chase
~ 50% Marine, ~ 50% Nonmarine
Lithofacies DistributionCouncil Grove, Panoma Field
26%
23%12%4%
8%7%16%
4%
NM Silt & Sd
NM Shly Silt
Mar Shale & Silt
Mudstone
Wackestone
Dolomite
Packestone
Grnst & PA Baf
(For A1 - B5)20% are bulk of pay
(Keystone wells)
Council Grove
Lithofacies
Nonmarine Shaly Siltstone
Core Slab
Cm
0.5 mm
T h i n S e c t i o n Photomicrograph
4.6%0.000024 md
Close-up Core Slab
0.5 mm
Thin Section Photomicrograph
M-CG Oncoid-PeloidGrainstone
Cm
21.2%32.3 md
Phyloid Algal Bafflestone
20.6%1141 md
Core Slab
Silty WackestoneCore Slab3.4%
0.0024 md
0.5 mm
Thin Section Photomicrograph
0.5 mm
Thin Section Photomicrograph
Close-up Core Slab
Dolomite13.9%1.1 md
Cm
Lithofacies classes and their depositional environment
Facies Stacking Patterns• Migrating facies belts
response to rapid glacial-eustatic SL fluctuation
• Facies vertically stacked in predictable manner
• Sequences bounded by exposure surfaces
• Facies log response predictable
• M-NM and Rel-Pos Geologic Constraining Variables helpful
Predict lithofacies at well scale with Neural networks• Select e-log predictor variables and
develop geologic constraining variables
• Train N-Nets on core lithofacies• Run N-Net models on 500 wells
and output facies curves in LAS format (automated, batch process)
• Import lithofacies curves files into geologic applications
Measuring error in test set predictions Core Lithofacies 6-8 and Predicted Lithofacies 6-8(Used PE when available)
0
50
100
150
2 6 10 14 18 22 26 30
X-Plot Log Porosity %
Freq
uenc
y
Core LithPred Lith
Porosity Distribution in Pay Lithofacies
Predicted Lithofacies “Scorecard” (Counts)
(Nnet mod1, no RelPos, w/ PE)
Effectiveness MetricsAbsolute accuracy
Accuracy within one facies
Proportional representation
Porosity (and perm) distribution in facies populations
Panoma Stratigraphic X-Sec
Neural network predicted lithofacies, 5 log variables, 1 geologic constraining variable
Property-based OGIPOGIP (property-based) is good early test of facies predicition, k-phi-Sw transforms and Phi correction.
OGIP = f (Sw,P, T, Z, Phi)
Sw = f (facies unique properties, Phi, FWL)
Automated; generate OGIP well curve in LAS format
Vol OGIPa & Cum Production Keystone Wells, Council Grove
0
1
2
3
4
ALEXANDER
BEATYKIM
ZEYNEWBY
LUKE
GU4SHANKLESHRIM
PLINSTUART
BC
FG
as
CumGasVol OGIPa
Using "best guess" FWL based on anecdotal data (perforations, tests, lowest producingperfs) and Pippin (1985). Cum gas is ~ 80% ERU.
Modified after Pippin (1985)
Generalized Field X-Section
Panoma
Hugoton
Gas-Water
Variable Free Water Level
Estimating free water level
Ratio OGIP50 / Cum Gas“Fault” map overlay
Ratio is relative to FWL
FWL can be back-calculated by est. OGIPmb and solving for FWL.
Related to minor faulting?
Cum Gas by Section 2002
Porosity HistogramNM Clastics
0%
5%
10%
15%
20%
25%
2 4 6 8 10 12 14 16 18 20 22 24Porosity (%)
Perc
ent o
f Pop
ulat
ions
NM Silt & Sand L1NM Shaly Silt L2
Porosity HistogramLimestones
0%
5%
10%
15%
20%
25%
30%
2 4 6 8 10 12 14 16 18 20 22 24Porosity (%)
Perc
ent o
f Pop
ulat
ions
Mdst/Mdst-Wkst L4Wkst/Wkst-Pkst L5Pkst/Pkst-Grnst L7Grst/Grst-Baff L8
Density log calibration
Porosity
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100Wetting Phase Saturation at 400' Above Free Water (%)
Wet
ting
Phas
e Sa
tura
tion
at H
eigh
t Abo
ve
Free
Wat
er L
evel
(%)
80 ft 2.18200 ft 1.39300 ft 1.15400 ft 1.00500 ft 0.91600 ft 0.84800 ft 0.74
Packstone Bafflestone
Permeability
logki=0.0588 logka3-0.187 logka2+1.154 logka-0.159logki=0.0588 logka3-0.187 logka2+1.154 logka-0.159
Mudstones-Bafflestones
0.00001
0.0001
0.001
0.01
0.1
1
10
100
0 2 4 6 8 10 12 14 16 18 20 22In situ Porosity (%)
In s
itu K
l. Pe
rmea
bilit
y (m
d)
Allmudstonemud-wackestonewackestonewacke-packstonepackstonepack-grainstonegrainstonebafflestone8
Mudstone-Wackestone Wackestone0.0001
0.001
0.01
0.1
1
0.0 0.2 0.4 0.6 0.8 1.0
Water Saturation (fraction)
Rel
ativ
e Pe
rmea
bilit
y (fr
actio
n)
w -10 mdg-10 mdw -1 mdg-1 mdg-0.1 mdw -0.1 mdg-0.01 mdw -0.01 mdg-0.001 mdw -0.001 md
Capillary Pressure300’ Above Free Water
Capillary Pressure Curves Pkst/Pkst-Grainstone(Porosity = 4-18%)
10
100
1000
0 10 20 30 40 50 60 70 80 90 100Water Saturation (%)
Gas
-Brin
e H
eigh
t Abo
ve F
ree
Wat
er (f
t)
Porosity=4%Porosity=6%Porosity=8%Porosity=10%Porosity=12%Porosity=14%Porosity=16%Porosity=18%
Gas and Water Relative Permeability
0.001
0.01
0.1
1
0 20 40 60 80 100Water Saturation (%)
Rel
ativ
e Pe
rmea
bilit
y (fr
actio
n)
8.08 md1.09 md0.72 md0.201md0.188md2907': 8 08 md
0.0001
0.001
0.01
0.1
1
0.0 0.2 0.4 0.6 0.8 1.0
Water Saturation (fraction)
Rel
ativ
e Pe
rmea
bilit
y (fr
actio
n)
w -10 mdg-10 mdw -1 mdg-1 mdg-0.1 mdw -0.1 mdg-0.01 mdw -0.01 mdg-0.001 mdw -0.001 md
Building the Structural Framework
Building the Structural FrameworkDefine grid increment and area of
interestConstruct top horizon for Council
Grove (A1_SH) top Create isochore for each zone, and
hang isochores from top horizonGenerate layers (define cell thickness)
Import wells with tops and logs11,367 total wells, 527 of which
contain log data10,840 wells with tops only (no logs)527 wells with tops, predicted facies
curves, “probability” curves, and porosity curves (Facies from two Nnet models, 352 with PE and 175 without PE)
Model Architecture
SH LM "Dummy" TotalA1 23 41 12 76B1 19 16 12 47B2 12 15 12 39B3 20 15 12 47B4 17 18 12 47B5 8 34 12 54C 28 61 12 101
Layers per ModelCells in modelXY = 1000 X 1000 feet
5,200 square mile model
7 Models (one per cycle)
Average model 8.6 million
Maximum 15 million (C cycle)
Minimum 5.7 million (B2 cycle)
Architecture QC
Structural Cross Section Grid
Panoma Facies Modeling
Facies ModelUp-scaled predicted facies to fit layeringBiased facies trends based on what we
know about the geology of the systemPopulated cells in between wells
(Sequential Gaussian Indicator)
Facies “Biasing”Non-biased
A1-LM Grnst-PA)
2:1 Trend Biased(NE-SW)
A1-LM Grnst-PA)
Heavy Trend Biased(NE-SW)
A1-LM Grnst-PA)
Facies Distribution Biased
B1-LM (Grnst-PAbaf)
Panoma Petrophysical Modeling
.0001
100101.1
.01.001
Petrophysical Models
Up-scaled porosity curve to fit layering and generated porosity model
Used perm facies transforms and porosity values in cells to generate permeability model
LithPhiNDUpscalePhiND Perm
Permeability in Facies 8, A1_LM
PermeabilityA1_LM
327 Layers
Upscale to Dynamic Model
Initial Simulation; single well
0.4 0.8 1.2 1.6 2.00
60
120
180
240
300ALEXANDER D2 - PANOMA
BHP/ZFlowing
0.4 0.8 1.2 1.6 2.00
60
120
180
240
300ALEXANDER D2 - PANOMA
Gas Cum (BCF)
BHP/ZFlowing
Pres
sure
We have just begun the transitionfrom a static model to the simulator,Beginning at the single well level.
Example from one of the key wells
1.00.75
0.75
1.0
Alexander D21.6 BCF cum.
10^2
10^3
10^4
10^5
10
1975
198 3
1 99 1
201 5
200 7
1 999
Mon
thly
Pro
d.
Initial dynamic models
Parameters• 640 Acre Section
• Cell Size: 390’ X 415’
Layers:
• Upscale from well – 6
• Geomodel – 41 (from 327)
• Well Location: Center
• 0.6’ X 315’ Fracture
• 100 Year Run
• Swc 30 %
• BHPi 260 psia
Three Runs1. Upscaled from well
2. GeoModel, Rate Specified
3. Geomodel, Pressure Specified
A1_LM
B1_LM
B2_LM
B3_LM
B4_LM
B5_LM
GR Facies
Core facies at single well
Perm, Layer 1
0.2 7.6Range (md)
Upscaled permeability from static model(map view layer 1)
Initial Simulation Results• Upscaled stochastic model performed better than one with upscaled well data alone
• Rate specified decline provided better match than pressure specified
Model (by Rate)
Well Model
2.6 BCF
1.4 BCF
Cum. Gas vs. Time
Summary; What’s next?
1. Many obstacles overcome by effort and automation.
2. Upscaling to more manageable model size for largerscale simulations (9, 81 wells).
3. Devise methodology to simulate on even larger scales.
4. On to the Chase (Hugoton) and into OK Panhandle