the hugoton geomodel: a hybrid stochastic-deterministic approach
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The Hugoton Geomodel: A Hybrid Stochastic-Deterministic Approach
Geoffrey C Bohling
Martin K Dubois
Alan P Byrnes
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 2
Study Area and History Largest gas field in North America.
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 (640-
850 m). Initial wellhead SIP 437 psi (3013 kPa) Dry gas, pressure depletion reservoir,
stratigraphic trap
Miles
Kilometers
10 0 10 20 30 40 50
0 20 6040 10020 80
COLORADO
KANSAS
OKLAHOMA
TEXAS
N
-500
0
0
500
500
500
1000
1500
1500
1000
1000
500
500
500
0
- 500
0
-1500
-1000
-500
0
1000
1000
500 0
-1000-500
Amarillo
Wichita
Uplift
Byerly
Bradshaw
Panoma
KansasHugoton
GuymonHugoton
TexasHugoton
WestPanhandle
EastPanhandle
38°
102°103°
35°
37°
36°
Legend
Oil productive area
Major faults
Gas productive areas
STUDYAREA
Study AreaPermian
(Wolfcampian) gas and oil fields
Wolfcamp Structure (CI=500’)(modified after Pippin, 1970, and Sorenson, 2005)
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 3
Stratigraphy Herington Limestone
Krider Limestone
Odell Shale
Winfield Limestone
Gage Shale
Towanda Limestone
Holmesville Shale
Ft Riley Limestone
Matfield Shale
Wreford Limestone
Speiser Shale
Funston Limestone
Blue Rapids Shale
Crouse Limestone
Easly Creek Shale
Middleburg Limestone
Hooser Shale
Grenola Limestone
Eskridge Shale
Cottonwood Limestone
Eiss Limestone Stearns Shale Morrill Limestone Florena Shale
Formation or Member
Ch
ase
Gro
up
Co
un
c il G
rove
Gr o
up
10
Lith
ofa
cie
s C
od
e
9
8
7
6
5
4
3
2
1
0
C_LM
A1_SH
A1_LM
B1_SH
B1_LM
B2_SH
B2_LM
B3_SH
B3_LM
B4_LM
B4_SH
B5_SH
B5_LM
C_SH
DE
PT
H (
ft)
Ss
Dol, mxln
Grnst
Pkst
Dol, fxln
Wkst
Mdst
Silt/sh
Fn Silt
Crs Silt
Ss
( fro
m c
or e
)C
on
tine
nta
l L
0,
L1
, L
2
Ma
rin
e
L3
- L
10
Flower A-1,Stevens Co., KS
Logged interval = 520 ft (160 m)
SYSTEM SERIES GROUPKansas fields
Oklahoma field
Leo
nard
ian
Sumner
Chase
Admire
Wabaunsee
Shawnee
Guymon- Hugoton
Greenwood
Per
mia
n
Wo
lfc
amp
ian
Pe
nn
syl-
van
ian
Virg
ilian
Council Grove
Hugoton-Panoma Byerly
Bradshaw
(compiled from Zeller, 1968; Pippin, 1970; Barrs et al., 1994; Merriam, 2006)
Production from 13 fourth order marine-continental cycles.
Shoaling upward carbonate cycles (reservoir) separated by redbed siltstones of poor reservoir quality.
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 4
Basic Problem
Inability to compute saturations from logs due to deep filtrate invasion
Significant differences in permeability-porosity 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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 5
Hugoton Geomodel 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)
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 6
Basic Workflow
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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 7
Neural Network Structure
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 8
Neural Network Parameter SelectionLooking 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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 9
Variation of Crossvalidation ResultsDifferent 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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 10
Variability of Neural Net PredictionsFive 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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 11
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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 12
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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 13
Submodel for Uncertainty AssessmentStratigraphically 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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 14
Varying Well Density RegionsEach 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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 15
Facies Variation at Synthetic Wells
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 16
Porosity Variation at Synthetic Wells
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 17
Perm, Sw, OGIPPermeability (k), Sw, and OGIP for each cell computed as functions of lithofacies and porosity ()
k – (Lith, )
Sw = f(Lith, , FWL)
0.00001
0.0001
0.001
0.01
0.1
1
10
100
1000
0 2 4 6 8 10 12 14 16 18 20 22 24 26
In situ Porosity (% )
Insi
tuK
lin
ken
ber
gP
erm
eab
ilit
y(m
d)
9 -crs sucros ic D o l3 -fn sucros ic D o lcrs sucros ic D o lfn sucros ic D o l
0.00001
0.0001
0.001
0.01
0.1
1
10
100
0 2 4 6 8 10 12 14 16 18 20 22 24 26In situ Porosity (% )
Insi
tuK
lin
ken
ber
gP
erm
eab
ilit
y(m
d)
0-N M vf sandstone1-N M crs s ilts tone2-N M vf-m ed s ilts tonevf S andstonecrs S ilts tonevf-m S ilts toneS ilts tones U nd if.
0.00001
0.0001
0.001
0.01
0.1
1
10
100
0 2 4 6 8 10 12 14 16 18 20 22 24 26In situ Porosity (% )
Insi
tuK
lin
ken
ber
gP
erm
eab
ilit
y(m
d)
8-gra in-/bafflestone7-pack/pack-gra instone5-wacke/wacke-packstone4-m ud/m ud-w ackestonebafflestonegrainstonepack-gra instonepackstonewacke-packstonewackestonem ud-wackestonem udstone
A
B
C
Mdst
Wkst
Pkst
fn-med sltstn
crs sltstn
vfn Ss
vfxln Dol
mxlnmoldic
Dol.
Grnst
k- relationships
Capillary Pressure Curves by Facies(Porosity = 10%)
10
100
1000
0 10 20 30 40 50 60 70 80 90 100
Water Saturation (%)
Ga
s-B
rin
e H
eig
ht
Ab
ov
e F
ree
Wa
ter
(ft)
1-NM Silt&Sand
2-NM Shaly Silt
3-Marine Sh & Silt
4-Mdst/Mdst-Wkst
5-Wkst/Wkst-Pkst
6-Sucrosic Dol
7-Pkst/Pkst-Grnst
8-Grnst/Grnst-PhAlg Baff
Capillary Pressure Curves Pkst/Pkst-Grainstone(Porosity = 4-18%)
10
100
1000
0 10 20 30 40 50 60 70 80 90 100
Water Saturation (%)
Ga
s-B
rin
e H
eig
ht
Ab
ov
e F
ree
Wa
ter
(ft)
Porosity=4%
Porosity=6%
Porosity=8%
Porosity=10%
Porosity=12%
Porosity=14%
Porosity=16%
Porosity=18%
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 18
Stabilization of OGIP Distribution
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 19
Overall Pore Volume, OGIP Variation
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 20
OGIP Variation by Data Density Area
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 21
Conclusions Study illustrates development of a lithofacies-based matrix properties model for
a giant gas field The 108-million cell, 169-layer geomodel was developed by:
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
Variations in OGIP estimates quite small, at least in areas of moderate to high data density
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
Long Beach, 2 April 2007
Bohling, Dubois, Byrnes 22
AcknowledgementsWe thank our industry partners for their support of the Hugoton Asset Management Project and their permission to share results of the study.
Anadarko Petroleum CorporationBP America Production Company
Cimarex Energy Co.ConocoPhillips Company
E.O.G. Resources Inc.ExxonMobil Production Company El Paso Exploration & Production
Osborn Heirs CompanyOXY USA, Inc.
Pioneer Natural Resources USA, Inc.
and Schlumberger for providing software
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