The Hugoton Geomodel: A
Hybrid Stochastic-Deterministic
Approach
Geoffrey C Bohling
Martin K Dubois
Alan P Byrnes
Study Area and
History
Largest gas field in North America.
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EUR 75 TCF (2.1 trillion m3)
12,000 wells, 6200 mi2 (16,000 km2).
2.8 BCF per well.
Spacing: 2-3 wells per 640 acres
Discovered 1922, developed 1940-50s.
Maximum continuous gas column: 500
ft (165 m).
Shallow: Top 2100-2800 ft deep (640850 m).
Initial wellhead SIP 437 psi (3013 kPa)
Dry gas, pressure depletion reservoir,
stratigraphic trap
Study Area
Permian
(Wolfcampian)
gas and oil fields
103°
Miles
102°
10
Byerly
0
10
20
30
40
50
Kilometers
20
0
20
40
60
80
100
Bradshaw
N
Panoma
38°
COLORADO
1500
0
500
Kansas
Hugoton
Legend
STUDY
AREA
Gas
productive
areas
Oil productive area
37°
KANSAS
1000
Major faults
OKLAHOMA
0
1500
Guymon
Hugoton
500
1000
Texas
Hugoton
TEXAS
-500
500
36°
1000
West
Panhandle
0
-1000
-1500
Amarillo
500
1000
1000
Wichita
- 500
500
0
East
Panhandle
0
Uplift 500
500
-500
-500
-1000
35°
0
Wolfcamp Structure (CI=500’)
-500
(modified after Pippin, 1970, and Sorenson, 2005)
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Continental L0, L1, L2
Marine L3 - L10
Formation or
Member
Herington Limestone
Krider Limestone
Odell Shale
Wolfcampian Leonardian
GROUP
Kansas
fields
Oklahoma
field
10
Chase
Council
Grove
HugotonPanoma
Byerly
Bradshaw
9
Holmesville Shale
Ft Riley Limestone
Wreford Limestone
GuymonHugoton
Wabaunsee
Greenwood
Shawnee
(compiled from Zeller, 1968; Pippin, 1970; Barrs et al., 1994; Merriam, 2006)
Ss
Towanda Limestone
Matfield Shale
Admire
Virgilian
Gage Shale
Sumner
Council Grove Group
SERIES
Pennsylvanian
SYSTEM
Permian
Shoaling upward carbonate cycles
(reservoir) separated by redbed
siltstones of poor reservoir quality.
Chase Group
Winfield Limestone
Speiser Shale
A1_SH
Funston Limestone
A1_LM
Blue Rapids Shale
B1_SH
Crouse Limestone
B1_LM
Easly Creek Shale
B2_SH
Middleburg Limestone
B2_LM
Hooser Shale
B3_SH
Eiss Limestone
Stearns Shale
Morrill Limestone
Florena Shale
B3_LM
B4_SH
Cottonwood Limestone
B5_LM
B4_LM
B5_SH
Eskridge Shale
C_SH
Grenola Limestone
C_LM
Dol,
mxln
8
Grnst
7
Pkst
6
Dol,
fxln
5
Wkst
4
Mdst
3
Silt/sh
2
Fn Silt
1
Crs Silt
0
Ss
(from core)
Production from 13 fourth order
marine-continental cycles.
Lithofacies Code
Stratigraphy
DEPTH (ft)
Flower A-1,
Stevens Co., KS
Logged interval = 520 ft (160 m)
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Basic Problem
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Inability to compute saturations from logs due
to deep filtrate invasion
Significant differences in permeabilityporosity and capillary pressure relationships
between facies
Prompts development of geomodel of entire
field for property-based evaluations of
volumetrics and flow
Supported by consortium of 10 companies
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Hugoton Geomodel
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108-million cell
Petrel model
Cells 660 ft x 660
ft (200 m x 200m)
and ~3 ft (1 m)
thick on average
11 lithofacies
Six submodels
(stratigraphically)
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Basic Workflow
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Neural network(s) trained on log-lithofacies
relationships in 27 cored wells (15 Chase, 16
Council Grove)
Lithofacies predicted in ~1600 logged wells
Sequential indicator simulation of lithofacies,
sequential Gaussian simulation of porosity
Permeability, capillary pressure, water
saturation from lithofacies-specific functions
of porosity and height above free water level
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Neural Network Structure
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Neural Network Parameter Selection
Looking for optimal
values of network size
and damping parameter
Each cored well
removed in turn from
training set
Neural net trained on
remaining wells;
predictions compared to
core in withheld well
Five trials per well and
parameter combination
Sundry measures of
prediction accuracy
computed
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Variation of Crossvalidation Results
Different symbol style
for each (withheld)
well; 5 trials per well;
14 wells (Upper
Chase)
Line is median, shown
on previous slide
Variability among wells
larger than variability
among parameter sets
On the other hand,
accuracy of
predictions not hugely
sensitive to choice of
parameters
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Variability of Neural Net Predictions
Five realizations
of neural net –
different initial
weights
Predicting on a
cored well
withheld from
training set
Some variability,
but big picture is
the same
This source of
variation not
pursued further;
one network
used
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Lithofacies Variograms
Variogram fitting problematic due to volume of data, number of facies (11)
and intervals (23), trends and/or zonal anisotropy
Upscaled data at wells exported from Petrel to R for automated analysis
Exponential variograms with zero nugget imposed by fiat; ranges estimated
for each facies and stratigraphic submodel (six of them)
Vertical fits mostly OK, horizontal fits . . . well, a little iffy
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Porosity Variograms
Porosity variograms generally rattier than facies variograms
Automatically estimated ranges for all variograms (facies and porosity)
then generalized/adjusted to reduced set of range values (by facies, one
set for Chase, another for Council Grove); ranges ~20-40 kft
SIS for facies, SGS for porosity – only one realization for full model
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Submodel for Uncertainty Assessment
Stratigraphically continuous model
for 2200 mi2 (5700 km2) east-west
“laydown” across middle of field; ~24
million cells
Assembled by Manny Valle, Oxy
200 realizations of entire workflow –
facies SIS, porosity SGS, property
and OGIP computations – saving
only OGIP
10 realizations saving all
intermediate properties
OGIP evaluated for whole model and
low-, medium-, and high-data density
regions
Properties examined at a synthetic
well in each of three regions
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Varying Well Density Regions
Each region is one township in
size (36 mi2, 93 km2)
Low density: 2 wells, both Chase
and Council Grove
Medium density: 9-14 Chase, 7-8
Council Grove
High density: 20-25 Chase, 20-22
Council Grove
Evaluation of data density effects
will be obscured somewhat by
variations in geological setting
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Facies Variation at Synthetic Wells
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Porosity Variation at Synthetic Wells
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Perm, Sw, OGIP
In situ Klinkenberg Permeability (md)
100
Permeability (k), Sw, and OGIP for
each cell computed as functions of
lithofacies and porosity (f)
vfn Ss
10
1
crs sltstn
0.1
0.01
0.001
0-NM vf sandstone
1-NM crs siltstone
2-NM vf-med siltstone
vf Sandstone
crs Siltstone
vf-m Siltstone
Siltstones Undif.
0.0001
0.00001
0
2
4
6
8
A
k – f(Lith, f)
Capillary Pressure Curves Pkst/Pkst-Grainstone
(Porosity = 4-18% )
1000
100
1-NM Silt&Sand
2-NM Shaly Silt
3-Marine Sh & Silt
4-Mdst/Mdst-Wkst
5-Wkst/Wkst-Pkst
6-Sucrosic Dol
100
Porosity=4%
Porosity=6%
Porosity=8%
Porosity=10%
Porosity=12%
Porosity=14%
8-Grnst/Grnst-PhAlg Baff
Porosity=18%
10
20
30
40
50
60
70
80
Water Saturation (%)
90 100
0.1
Pkst
0.01
8-grain-/bafflestone
7-pack/pack-grainstone
5-wacke/wacke-packstone
4-mud/mud-wackestone
bafflestone
grainstone
pack-grainstone
packstone
wacke-packstone
wackestone
mud-wackestone
mudstone
0.001
0.0001
0
2
4
6
8 10 12 14 16 18
In situ Porosity (%)
20 22
Wkst
24 26
1000
Porosity=16%
10
1
B
7-Pkst/Pkst-Grnst
10
Grnst
10
0.00001
In situ Klinkenberg Permeability (md)
Gas-Brine Height Above Free Water (ft)
Gas-Brine Height Above Free Water (ft)
1000
In situ Klinkenberg Permeability (md)
Sw = f(Lith, f, FWL)
0
10 12 14 16 18 20 22 24 26
In situ Porosity (%)
100
Capillary Pressure Curves by Facies
(Porosity = 10% )
fn-med
sltstn
Mdst
100
10
1
mxln
moldic
Dol.
0.1
0.01
0.001
9-crs sucrosic Dol
3-fn sucrosic Dol
crs sucrosic Dol
fn sucrosic Dol
0.0001
0.00001
0
10
20
30
40
50
60
70
Water Saturation (%)
80
90 100
0
C
2
4
6
8
10 12
14 16 18 20 22 24 26
In situ Porosity (%)
vfxln
Dol
k-f relationships
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Stabilization of OGIP Distribution
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Overall Pore Volume, OGIP Variation
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OGIP Variation by Data Density Area
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Conclusions
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Study illustrates development of a lithofacies-based matrix properties model for
a giant gas field
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The 108-million cell, 169-layer geomodel was developed by:
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Defining lithofacies in 1600 wells with neural network models trained on core
lithofacies-to-log correlations
Modeling between wells using sequential indicator simulation (SIS) for lithofacies and
sequential gaussian simulation (SGS) for porosity
Calculating permeability, capillary pressure, and relative permeability for each unique
lithofacies-porosity combination using empirical transforms
Calculating water saturation using the lithofacies/porosity-specific capillary pressure
and a location-specific height-above-free-water level

Because horizontal ranges for estimated variograms (20-40 kft) are > than
node well spacing (~1-3 kft), expected multiple realizations from stochastic
simulations to be nearly deterministic; perhaps approaching that where well
density is high
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Variations in OGIP estimates quite small, at least in areas of moderate to high
data density
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The Hugoton geomodel illustrates the continuum between stochastic and
deterministic modeling and the dependence of the methodology used for each
property on the available data, the scale of prediction, and the order
(predictability) of the system relative to the property being modeled
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Acknowledgements
We thank our industry partners for their support of the Hugoton Asset
Management Project and their permission to share results of the study.
Anadarko Petroleum Corporation
BP America Production Company
Cimarex Energy Co.
ConocoPhillips Company
E.O.G. Resources Inc.
ExxonMobil Production Company
El Paso Exploration & Production
Osborn Heirs Company
OXY USA, Inc.
Pioneer Natural Resources USA, Inc.
and Schlumberger for providing software
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