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Geologic model for the giant Hugoton and Panoma Fields Martin K. Dubois

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Geologic model for the giant Hugoton and Panoma Fields Martin K. Dubois
Geologic model for the giant
Hugoton and Panoma Fields
Martin K. Dubois
Alan P. Byrnes
Geoffrey C. Bohling
Midcontinent AAPG, Oklahoma City
September 13, 2005
upscale porosity
Objectives of modeling project
Objective: Build 3D cellular model
populated with lithofacies and
petrophysical properties
Purpose:
1.
2.
Identify and quantify remaining
gas in order to develop best field
practices for efficient recovery.
Study sedimentary response to
rapid glacio-eustatic sea level
fluctuations on an extremely
gently sloped ramp (shelf).
More specifically, and in conjunction
with simulations studies
ƒ Estimate original gas in place at
well, region and field scales
ƒ Reservoir connectivity at pore,
flow unit, well, inter-well, region
and field scales
ƒ Differential depletion in
stratigraphically separate reservoirs
ƒ Production decline rates and EUR
at ultra low pressures
Status and outline
Modeling project status:
To be covered today:
ƒ
9
Model workflow
9
Major lithofacies and
depositional model
9
Large scale geometry of
Hugoton and Panoma
9
Lithofacies in maps and
cross sections
ƒ
Township scale models have
been built and tested by
numerical simulation
Components are in place for
building field-wide cellular
model and work is underway
(Field 3D model not yet
complete but plenty to see)
Thinly layered, alternating carbonate and
siltstone reservoir in 13 marine-nonmarine
sedimentary cycles
Herrington
Krider
Winfield
Towanda
550 ft
(Hugoton)
(Panoma)
Chase Group
Council Grove Gp.
Wolfcampian
130 Miles
Hugoton and Panoma
Stratigraphy
L. Permian
Ft Riley
Florence
Wreford
Funston
Crouse
Middleburg
Eiss
Morrill
Cottonwood
Neva
Geomodel Workflow (static model)
Gather data
CORE &
ELog Var.
Neural Net
NODE WELLS
Stochastic
Methods
3D MODEL
1400 “Node” Wells
Train Neural network and
predict lithofacies in non
cored wells (nodes)
Lithofacies in core tied to
log and geologic
constraining variables
Fill volume between
node wells using
stochastic methods
Develop dynamic model through
empirical relationships
Empirical
Relations &
Free Water
Level
Permeability,
Water sat.,
Rel. Perm.
Differential
Pressure,
Corrected
MatBal OGIP
Dynamic
Model &
Simulation
Relative Permeability (fraction)
Model,
Facies,
Phicorr
1
0.1
0.01
w-10 md
g-10 md
w-1 md
g-1 md
g-0.1 md
w-0.1 md
g-0.01 md
w-0.01 md
g-0.001 md
w-0.001 md
0.001
0.0001
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Dubois, Byrnes etal, 2003
Water Saturation (fraction)
Lithofacies from Core to “Node” Wells
80 mi
130 km
Current training set
Other
Some wells have both Chase
and Council Grove core
130 mi
210 km
27 mi
43 km
Training set for
neural network
lithofacies prediction
Well 1/2 foot
count intervals
8
3952 Chase
10
4593 Council
Grove
8545 ½-foot intervals
with lithofacies tied to
log and core properties
Lithofacies predicted
at 1369 “node wells”
Neural Network Training and Predictions
Chase
All
1369 Wells
Predicted
Sandstone
8%
4%
6%
2%
Coarse Silt
28%
23%
23%
20%
Fine Silt
24%
4%
13%
8%
Siltstone
9%
7%
8%
10%
Carb Mdst
7%
5%
5%
4%
Wackestone
18%
13%
14%
19%
Fxln Dol.
4%
2%
3%
4%
Packstone
15%
17%
15%
23%
Grainstone
4%
1%
2%
0%
M-Cxln Dol.
0%*
12%
6%
4%
Sandstone
0%**
12%
5%
6%
Predicted
Council
Grove
Training
Tr
ain
Pr
ed
ic
Facies
Continental
Distribution of eleven
lithofacies in training set
t
Lithofacies in training set
and predicted in wells
42% 30%
Marine
27% 33%
31% 37%
* Insufficient training sample. Combined with Fxln Dolomite
** Insufficient training sample. Combined with Siltstone.
Distribution of lithofacies
predicted in 1369 wells is
similar to that in training set.
M-Cxln MM SS
5%
Dol 6%
Cont SS
6%
Grnst
2%
Cont
Crs Slt
23%
Pkst
15%
Fxln Dol
3%
Cont
Fn Slt
13%
Wkst
14%
Mdst
5%
Mar Slt
8%
Phyloid Algal
Bafflestone
Close-up Core Slab
Nonmarine
Shaly Siltstone
0.5 mm
4.6%
0.000024 md
Nonmarine Crs
Siltstone-vfg SS
Cm
Core Slab
Thin Section Photomicrograph
T h i n S e c t i o n Photomicrograph
L8
Core Slab
20.6%
1141 md
Dolomite
13.9%
1.1 md
C l o s e -u p C o r e Slab
Close-up Core Slab
10.8%
0.30 md
Thin Section Photomicrograph
Council
Grove
Lithofacies
Cm
L6
L1-2
Pellet
Grainstone
13.0%
2.53 md
0.5 mm
L0
0.5 mm
Core Slab
1
’s
0
l
ain
l
a
id
M
K
(1
t
la
F
M
’s
0
)
s
ile
T
astlP
n
o
C
ago
L
lM
ound
ga
yl.A
h
P
e a
Id
liz d
e
e
D
o p
M
s
lo
e
d ito
n la
o
(M
d
a
ife
s
rR
fte
,I
is
o
rv
n
.)
c
Marine
Siltstone
Thin Section
Photomicrograph
10.4 %
0.01 md
Thin Section Photomicrograph
0.5 mm
L7
L8
0.5 mm
L3
(time slice)
0.5 mm
Silty Wackestone
Thin Section Photomicrograph
Thin Section Photomicrograph
M-CG Oncoid-Peloid
Packstone
21.2%
32.3 md
Close-up Core Slab
Cm
L5
Skeletal
Wackestone
2.1%
0.11 md
0.5 mm Close-up Core Slab
Thin Section
Photomicrograph
L4
3.4%
0.0024 md
0.5 mm
Core Slab
Unique Chase Lithofacies
Two additional lithofacies plus same nine
as in Council Grove but in different
proportions. No phylloid algal facies.
Close-up
Core Slab
2 cm
Close-up Core Slab
Crs XLN Dolomite
(CG oo-grnst)
22.3%
275 md
Marginal Marine
FG Sandstone
20.8%
48.2 md
Thin Section
Thin Section
(time slice)
Dolomitized medium to coarse-grained ooid and bioclastic
grainstone are the dominant reservoir facies in Chase
Present Day Structure
100 mi (160 km)
Panoma
KE
YE
SD
OM
E
Hugoton
Reservoirs of Hugoton and
Panoma Fields were deposited on
a very gently dipping shelf. Relief
was much less than it is today.
Top Council Grove
Shelf
Margin
Chase
Base of
Council Grove
VE = 200X
VE = 100X
Chase and Council Grove
Core facies
700 ft
215 m
Council Grove
Chase
Field
Margin
Continental
VE=700X
Marine
Silt
Grain support carb.
Sand
Mud supported and silt
85 miles
135 km
Shelf
Margin
9
Carbonate thins
toward updip
field margin
9
Redbeds thin
basinward
9
Eolian sands at
west margin
9
Council Grove
thinnest at midshelf
Gross
interval
0’
2
2
Net
“Continental”
Net
Marine
70
’
15
0’
Similar sedimentation
patterns in Chase
and Council Grove
0’
7
1
37
0’
’
40
0’
22
’
30
25
0’
Series of slides based
on facies predicted by
Nnet in 1369 wells
0’
1
2
Th
Mi inn
ds es
he t
lf
Council Grove
(thru B5_LM)
30
40
’
0’
Chase
Mean Lithofacies in Marine Intervals
Entire Chase
Entire Chase
8
Color
Bar
Scale
4
Chase to Ft Riley
Council Grove to C_SH
8
8
4
4
6.5
4.5
Facies 3-10
Mean F = 6.7 SD = 0.9
F10 dominates west
margin
5.8
6.7
Facies 3-9
Mean = 5.8 SD = 0.6
Marine
3
Siltstone
4
Carb Mdst
5
Wackestone
6
Fxln Dol.
7
Packstone
8
Grainstone
9
M-Cxln Dol.
10
Sandstone
Facies 3-9
Mean = 5.8 SD = 0.9
F9 dominates south
Facies 3-9
Mean = 5.4 SD = 0.4
F6 dominates to NE
F7 dominates to SE
Continental
0
Sandstone
1
Coarse Silt
2
Fine Silt
Shown are the mean code value
for lithofacies predicted by neural
network models in 1350 wells
Main “Pay” Lithofacies in Chase (F7-9)
Herrington
Krider
Winfield
Towanda
Krider only PhiH for F9
(Herrington through Gage)
200
0.8
0
0
Phi x H for Facies 9
Cutoff phi >15%
Net thickness
Facies 7 thru 9
Net / Gross
Facies 7 thru 9
Accumulation of coarsegrained bioclastic-ooid
sand associated with
bathymetry of embayment
near the shelf margin
Krider Ooid shoal facies in
Stevens County
A
A’
10 foot divisions
Core
A
2 cm
A’
Close-up Core Slab
Crs XLN Dolomite
(CG oo-grnst)
22.3%
275 md
Thin Section
1
Coarse Silt
3
Siltstone
4-5 Mdst-Wackestone
7
Pack-Grainstone
9
M-Cxln Dol.
10
Sandstone
Cottonwood (B5_LM) Phylloid Algal Mounds
Net H, F7-8, Phi >10%
Phyloid Algal
Bafflestone
20
A
0
L8
Core Slab
20.6%
1141 md
0
1
Sandstone
Coarse Silt
2
3
Fine Silt
Siltstone
4-5 Mdst-Wackestone
6
Fxln Dol.
7-8 Pack-Grainstone
A’
A
Core
A’
Crouse (B1_LM) fine-crystalline dolomite lithofacies
F6-8, phi > 8%, Net/Gross
0.8
0
1
Sandstone
Coarse Silt
2
3
Fine Silt
Siltstone
4-5 Mdst-Wackestone
A
6
0
Fxln Dol.
7-8 Pack-Grainstone
B
A
Core
0.5 mm
B
Thin Section Photomicrograph
Dolomite
13.9%
1.1 md
C l o s e -u p C o r e Slab
Cm
L6
Core
Neva
(C_LM)
0
1
Sandstone
Coarse Silt
2
3
Fine Silt
Siltstone & Sandstone
4-5 Mdst-Wackestone
6
Top
Council
Grove
Neva
Fxln Dol.
7-8 Pack-Grainstone
Net thickness, phi >15%
Fine-grained
sandstone in
lower Council
Grove is pay in
Texas County
Eolian sandstone
Council Grove
Continental
sandstone
thickness
120
Cum.
Prod.
1.5
BCF
20
Dubois and Goldstein, 2005
Summary
ƒ
Township scale models have been built and
tested by numerical simulation
ƒ
Components are in place for building field-wide
cellular model (underway)
ƒ
Neural network models are proving effective in
facies predictions and building an accurate
geomodel
ƒ
We anticipate being able to successfully
delineate remaining gas in place in the Hugoton
and Panoma Fields
Acknowledgements
We thank our industry partners for their support
of the Hugoton Asset Management Project and their
permission to share the results of the study.
Anadarko Petroleum Corporation
BP America Production Company
Cimarex Energy Co.
ConocoPhillips Company
E.O.G. Resources Inc.
Medicine Bow Energy Corporation
Osborn Heirs Company
OXY USA, Inc.
Pioneer Natural Resources USA, Inc.
also geoPlus (Petra) and Schlumberger (Petrel)
Fly UP