combined reservoir simulation and seismic technology; … conclusions and learning seismic...
TRANSCRIPT
Combined Reservoir Simulation and
Seismic Technology;
A New Approach for Modeling CHOPS
Hossein Aghabarati / University of Calgary
Carmen Dumitrescu / Sensor Geophysical LTD.
Larry Lines / University of Calgary
Antonin Settari /University of Calgary
SPE/PS/CHOA 117581 PS 2008-317
Presentation Outline:
• CHOPS Wells Overview
• Modeling Strategy
• Structural Modeling (Seismic)
• Rock and Fluid Properties
• Modeling Results
• Learning's and Conclusions
CHOPS Overview
• 100 years of CHOPS operation
• Initially was developed in California
• Has been a major recovery method for producing heavy oil in Western Canada
• Oil rates are not justified thorough Darcy Flow Equation
• PCP pumps application in late 80’s made the process more economically viable
• In 2002 around 20 % of Canada’s production was from CHOPS
• Relatively inexpensive
• Sand management issues
Heavy Oil prospects in Canada
"plentiful" oil
(AEUB Web Site)
Understanding CHOPS Operations
(AEUB Web Site)
Modeling Strategy
Coupled geomechanics models based on the
erosion theory
Wang, J (2003)
Plover Lake Field
Reservoir Structural Modeling
Rp and inserted synthetic traces for several wells and (b) density results. Inserted in
color are the density logs. All the wells tie this line within a 60 m projection
distance. (W: Waseca, MB: Mid Bakken, LB: Lower Bakken, T: Torquay)
(Carmen C. Dumitrescu, 2008; SENSOR TECHNOLOGY FORUM)
Reservoir Structural Modeling
04-09 well
Geostatistical Methods Used for Estimating Porosity
c d
Porosity maps based on:
(a) kriging
(b) cokriging method
(c) KED
(d) KED with porosity from multiattribute analysis result as the secondary variable.
Initial Porosity Map
04-09
591,400 591,500 591,600 591,700 591,800
591,400 591,500 591,600 591,700 591,800
5,7
59,9
00
5,7
60,0
00
5,7
60,1
00
5,7
59,9
00
5,7
60,0
00
5,7
60,1
00
5,7
60,2
00
0.00 205.00 410.00 feet
0.00 65.00 130.00 meters
0
3
7
10
13
16
19
22
26
29
32
CMGLTemp Prop1 1901-01-01 K layer: 1
Permeability Model
k = 871305f5.338
R2 = 0.4282
0
500
1000
1500
2000
2500
3000
0.25 0.26 0.27 0.28 0.29 0.3 0.31 0.32 0.33 0.34 0.35
Porosity [Fraction]
Per
mea
bil
ity
[m
D]
KMax (mD)
Power (KMax (mD))
Modeling Permeability Heterogeneity
Rock and Fluid Properties
Sand Production Controls
Parameter Value assigned
Initial fluidized sand saturation 0.08
Maximum porosity 0.65
Velocity coefficient Water 1.00
Velocity coefficient Oil 1.00
Velocity coefficient Gas 0.00
Critical velocity for onset of sand production (m/S)
0.00
Sand slip coefficient 1.00
Initial erosion coefficient (1/m) 5.00
Initial deposition coefficient (1/m)
0.00
Critical fluidized sand saturation 0.15
Time scale exponent n 0.50
Oil Matches
04-09 well
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
May-79 May-82 May-85 May-88 May-91 May-94 May-97 May-00 May-03 May-06 May-09
date
Oil R
ate
m3/d
Model using Sand Pro History Data Model No Sand Pro
04-09 well
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
May-
79
May-
82
May-
85
May-
88
May-
91
May-
94
May-
97
May-
00
May-
03
May-
06
May-
09
date
Cu
m O
il m
3
History Data Model using Sand Pro Model No Sand Pro
Start of
Aggressive
Drawdown (PCP)
Average Reservoir Pressure and Sand Production Estimations
04-09 well
0
1000
2000
3000
4000
5000
6000
7000
May-
79
May-
82
May-
85
May-
88
May-
91
May-
94
May-
97
May-
00
May-
03
May-
06
May-
09
date
Avera
ge R
eserv
oir
Pre
ssu
re (
kP
a)
Model using Sand Pro Model No Sand Pro
04-09 well
0
2
4
6
8
10
12
14
16
May-
79
May-
82
May-
85
May-
88
May-
91
May-
94
May-
97
May-
00
May-
03
May-
06
May-
09
date
Sa
nd
ra
te (
m3
/d)
0
2000
4000
6000
8000
10000
12000
14000
16000
Cu
m S
an
d (
m3
)
Sand rate Cum Sand
Start of aggressive
drawdown (PCP)
Results
Initial permeability Initial porosity Final Porosity
Time Lapsed Seismic Results
Time lapsed porosity cahnge
Gas saturation map
Areas which show high correlation between
seismic response and gas saturation
Conclusions and Learning
Seismic attributes and geostatistic method (KED), helped in
creating a more accurate structure map for reservoir simulation .
KED helped for the initial estimation of the reservoir simulation
parameters, e.g. porosity (honor both seismic data and the well
log data)
The seismic attributes are more sensitive to the change in gas
saturation rather than change in porosity. This was validated
through this study using Plover Lake time lapsed data.
In general, grid blocks with higher porosity and lower structure
experienced more porosity change (higher wormholes density).
Future Work:
Full Field Modeling
Post CHOPS EOR for improving recovery factor
Acknowledgments
• CHORUS (Sponsors and Team)
• SENSOR GEOPHYSICAL LTD
• NEXEN Inc
• Halliburton & TAURUS Reservoir Solutions Ltd
Thank You