The document provides an outline and objectives for constructing a reservoir model from well data using stochastic simulation. It presents well location and data quality control results, including boxplots and histograms of porosity and permeability which show uniform porosity distributions and lognormal permeability distributions with some outlier data. A top reservoir surface is generated through stochastic simulation after removing regional eastward dipping trend and faults identified from compartmentalization.

1. Yien Fuang Tiong
Hasan Jehanzaib
Yaseen Bokhamseen
Fei Zhao
Zalani Kamarudin
Karthik Surisetty
Group A

2. Outline
• Objectives
• Well data overview
• Well data quality control
• Top reservoir realisation
• Bulk rock volume (BRV) and pore volume in
hydrocarbon zone (HPV) estimations
• Fine scale model BRV, NTG and HPV
• Reservoir model upscaling and volume
estimation
221/04/2015

3. Objectives
• Using stochastic simulation, construct
reservoir model from well data
• Volume estimation
• Investigate the accuracy of upscaling via
volume comparison
21/4/2015 3

4. Well locations
21/4/2015 4
Well locations
X Location
YLocation
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
No well data in
the circled areas!
14 exploration
wells were drilled

5. Comments regarding wells
• Well data covers a wide area of reservoir,
especially in the east and south-west
• Lacking data in north-west and left centre of
the reservoir
• Affects reliability of reservoir model at those
areas
• All wells intersect oil-water contact and
bottom of the reservoir. All contacts are
shallower than reservoir bottom
21/04/2015 5

6. Well data summary
21/4/2015 6
Well no. x (m) y (m) top (m) bottom (m) owc (m)
1 1603 729 3045 3149 3083
2 1617 1782 3052 3141 3083
3 1515 1403 3052 3141 3083
4 1690 573 3044 3147 3083
5 1605 1363 3052 3141 3083
6 916 257 3030 3122 3083
7 812 1548 3039 3134 3083
8 1293 972 3048 3144 3083
9 540 567 3007 3095 3083
10 238 660 2974 3085 3083
11 1770 1608 3052 3141 3083
12 1211 645 3043 3147 3083
13 1226 1663 3050 3140 3083
14 1441 1302 3052 3141 3083

7. Well Data Quality Control
• Only well data is provided for stochastic
simulation
• Important to conduct quality check
• For properties like porosity and permeability,
check for trends in x and y directions
• Check quality of data
21/4/2015 7

8. Porosity boxplots sorted in x direction
21/4/2015 8
0
0.05
0.1
0.15
0.2
0.25
1
2
3
4
5
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
X Location (m)
Porosity(frac)
Porosity boxplots sorted in X direction
• Facies 1 and 3 are low porosity formations
• No obvious trends in x direction for facies 2, 4 and 5
• No outlier data

9. Porosity boxplots sorted in y direction
21/4/2015 9
0
0.05
0.1
0.15
0.2
0.25
1
2
3
4
5
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
Y location (m)
Porosity boxplots sorted in y direction
Porosity(frac)
• No obvious trends in y direction for facies 2, 4 and 5

10. Permeability boxplots sorted in x direction
21/4/2015 10
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1
2
3
4
5
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
237.821
539.595
812.111
916.467
1211.22
1225.85
1293.4
1440.6
1514.67
1603.16
1605.01
1617.03
1689.69
1770.38
X location (m)
Permeability(D)
Permeability boxplots sorted in x direction
• No obvious trends in x direction for facies 2, 4 and 5
• Outlier data present beyond the upper whisker. No extreme data
Outliers!

11. Permeability boxplots sorted in y direction
21/4/2015 11
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1
2
3
4
5
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
256.614
567.49
573.244
644.709
659.552
728.887
971.943
1302.44
1362.88
1402.88
1548.47
1608.43
1663.3
1782.3
Y location (m)
Permeability(D)
Permeability boxplots sorted in y direction
• No obvious trends in y direction for facies 2, 4 and 5
• Outlier data present beyond the upper whisker. No extreme data
Outliers!

12. Question to Ask?
• Is the permeability outlier data dubious?
• Conduct further quality check to find out
21/4/2015 12

13. Porosity-permeability qqplots (all wells)
21/4/2015 13
0 0.05 0.1 0.15 0.2 0.25
-0.1
0
0.1
0.2
0.3
0.4
Porosity (frac)
Permeability(D)
Facies 2 porosity & permeability qqplot
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18
-0.1
0
0.1
0.2
0.3
0.4
Porosity (frac)
Permeability(D)
Facies 4 porosity & permeability qqplot
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
Porosity (frac)
Permeability(D)
Facies 5 porosity & permeability qqplot
• Porosity and permeability distributions are
similar for most parts of data
• Dissimilarity is more severe at the higher end
of the data
• Strong indication that porosity and
permeability do not have the same
distribution

14. Porosity histograms (all wells)
21/4/2015 14
0 0.05 0.1 0.15 0.2 0.25
0
5
10
15
20
25
30
Porosity (frac)
Frequency
Facies 2 porosity histogram
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18
0
5
10
15
20
Porosity (frac)
Frequency
Facies 4 porosity histogram
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
0
5
10
15
20
25
30
35
Facies 5 porosity histogram
Porosity (frac)
Frequency
• Porosity for each facies has uniform
distribution

15. Permeability histograms (all wells)
21/4/2015 15
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
0
5
10
15
20
25
30
35
Facies 2 permeability histogram
Permeability (D)
Frequency
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
0
5
10
15
20
25
30
Permeability (D)
Frequency
Facies 4 permeability histogram
0 0.05 0.1 0.15 0.2 0.25
0
10
20
30
40
50
Permeability (D)
Frequency
Facies 5 permeability histogram
• Permeability for each facies has lognormal –
like distribution
• Likely reason for the permeability outlier
data beyond the boxplots’ upper whiskers

16. Porosity-permeability scatter plots
21/4/2015 16
0 0.05 0.1 0.15 0.2 0.25
0
0.5
1
1.5
2
2.5
3
Facies 2 porosity-log(permeability) cross plot
Porosity (frac)
Log(permeability)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18
-0.5
0
0.5
1
1.5
2
2.5
3
Facies 4 porosity-log(permeability) cross plot
Porosity (frac)
Log(permeability)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14
0
0.5
1
1.5
2
2.5
Facies 5 porosity-log(permeability) cross plot
Porosity (frac)
Log(permeability)
• High likelihood of correlations between
porosity and log(permeability)
• No outliers in these plots
• Permeability data is not dubious

17. Top reservoir realisation
• Use stochastic simulation to generate a top
surface
• Important to remove trend
• Information from regional geology
– Reservoir dips gently to the east
– Reservoir is strongly compartmentalised,
indicating presence of faults
• Expect trend in east-west (x) direction and
trend arising from fault compartmentalisation
21/04/2015 17

18. Well top markers
sorted in x direction
21/04/2015 18
2970
2980
2990
3000
3010
3020
3030
3040
3050
3060
20 40 60 80 100 120 140 160 180
Top(m)
x grid no.
x grid no. vs. top
Faults
Grid 50
Shift ~ 20m
Grid 150
Shift ~ 10m

19. Detrending fault compartmentalisation
• Assume middle compartment is the hanging
wall to the two foot walls at east and west
– Shift middle compartment upwards
• Assume linear increase in shift from west to
east (between grid 50 and 150)
• For x grid between 50 and 150,
vertical shift = 25 - 0.1(X grid no.)
21/04/2015 19

20. Top markers after removing fault trend
21/4/2015 20
Top = 0.5008x + 2968.3
R² = 0.9387
2970
2980
2990
3000
3010
3020
3030
3040
3050
3060
20 40 60 80 100 120 140 160 180
Depth(m)
X grid no.
Top markers without fault trend
Top markers dipping from west to east

21. Top markers residuals
21/4/2015 21
-10
-5
0
5
10
15
0 50 100 150 200
Residual(m)
X grid no.
Top marker residuals sorted in x
-10
-5
0
5
10
15
0 50 100 150 200
Residual(m)
Y grid no.
Top marker residuals sorted in y
Top markers residuals do not have trends in x and y directions
 Ready for stochastic simulation

22. Residual Tops stochastic simulation
21/4/2015 22
X
Y
Residuals map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
-10
-5
0
5
10
Stochastic simulation for 500 points (range = 40, sill = 10), then interpolate residuals using
inverse distance interpolation (with power 2)

23. Top reservoir map after restoring x
direction trend and faults
21/4/2015 23
X
Y
Top Reservoir Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200 2960
2970
2980
2990
3000
3010
3020
3030
3040
3050
3060

24. Bulk rock volume calculation
• Since oil-water contact (owc) is within the
reservoir, it is assumed to be the bottom of
the reservoir
• Area of each cell is 10m by 10m
• Reservoir thickness at each location = owc –
top
• Bulk rock volume = 231MMm3
21/4/2015 24

25. Pore volume in hydrocarbon zone (HPV)
• HPV estimation requires net-to-gross and
porosity values
• Approach: estimate net volume and average
sandstone porosity at each well location
– Assume facies 2, 4 and 5 are clean sandstone 
net-to-gross of 1
– Facies 1 and 3 are pure shale  net-to-gross of 0
• Use stochastic simulation, generate net
volume map and average porosity map
21/04/2015 25

26. Cartesian to Structural Grids
21/4/2015 26
Assume dip in x-direction only - yield deeper structural x-coordinate location vs cartesian.
Estimated dip angle (~3 degree)

27. Estimating average porosity
21/4/2015 27
•Use defined structural coordinate
•Run stochastic simulation for parts of the grid
•Use well data average porosity
•Populate the rest of the grid by inverse distance method
Calculated Average sandstone porosity = 0.0942

28. Porosity map
21/4/2015 28
Stochastic Average sandstone porosity = 0.085

29. Net volume at well locations
21/4/2015 29
NV = -53.53x + 8745.9
R² = 0.9315
1000
2000
3000
4000
5000
6000
7000
8000
20 40 60 80 100 120 140 160 180
NetVolume(m3)
X grid no.
Net volume sorted in x direction
Net volume reduces until grid 120

30. Net volume residuals without x trend
21/4/2015 30
-1400
-1200
-1000
-800
-600
-400
-200
0
200
400
600
0 50 100 150 200
NetVolumeResidual(m3)
X grid no.
Net volume residuals sorted in x
NVres = -3.6007y + 494.31
R² = 0.8235
-1400
-1200
-1000
-800
-600
-400
-200
0
200
400
600
0 50 100 150 200
NetVolumeResidual(m3)
Y grid no.
Net volume residuals sorted in y
Net volume residuals without x trend has a trend in y direction
 Remove the trend in y direction

31. Net volume residuals without x and y
trends
21/4/2015 31
-1400
-1200
-1000
-800
-600
-400
-200
0
200
400
0 50 100 150 200
NetVolumeResidual(m3)
X grid no.
Net volume residuals sorted in x
-1400
-1200
-1000
-800
-600
-400
-200
0
200
400
0 50 100 150 200
NetVolumeResidual(m3)
Y grid no.
Net volume residuals sorted in y
After removing x and y trends, net volume residuals do not have trends in x and y directions
 Ready for stochastic simulation

32. Net volume residual map
21/4/2015 32
Stochastic simulation for 704 points (range = 40, sill = 10), then interpolate residuals using
inverse distance technique (with power 2)
X
Y
Net Volume Residuals Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
-1000
-800
-600
-400
-200
0

33. Net volume map after restoring trends
21/4/2015 33
Total net volume = 166.7MMm3
Reservoir NTG = 0.7216
X
Y
Net Volume Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
2000
3000
4000
5000
6000
7000
8000
9000

34. Hydrocarbon pore volume (HPV)
21/4/2015 34
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200 100
200
300
400
500
600
700
800
900
1000
HPV= 13.45 MM m3

35. Fine scale model volume estimation
• Using similar methods to estimate BRV, HPV,
reservoir NTG and average reservoir
sandstone porosity
21/4/2015 35

36. Fine scale model BRV
21/4/2015 36
X
Y
Top Reservoir Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
2980
2990
3000
3010
3020
3030
3040
3050
3060
BRV = 183.6MMm3

37. Fine scale NTG map
21/4/2015 37
X
Y
Net To Gross Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
Reservoir NTG = 0.7203

38. Fine scale porosity map
21/4/2015 38
X
Y
Average Sandstone Porosity Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200 0.06
0.08
0.1
0.12
0.14
0.16
Average reservoir sandstone porosity = 0.1101

39. Fine scale HPV map
21/4/2015 39
X
Y
Map of Pore Volume in Hydrocarbon Zone
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
200
300
400
500
600
700
Pore volume in hydrocarbon zone = 14.3MMm3

40. Upscaling
• Upscale fine scale model from 2003 grids to
203 grids
• Upscale top reservoir map to calculate
upscaled BRV
• Use facies values to upscale NTG
• Use arithmetic average to upscale porosity
21/4/2015 40

41. X
Y
Top Reservoir Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
2980
2990
3000
3010
3020
3030
3040
3050
3060
Upscaled top reservoir map
21/4/2015 41
X
Y
Upscaled Top Reservoir Map
2 4 6 8 10 12 14 16 18 20
2
4
6
8
10
12
14
16
18
20
2980
2990
3000
3010
3020
3030
3040
3050
3060

42. Bulk rock volume estimation
• BRV is the same for fine and coarse scale
models = 183.6MMm3
• Similarity is due to method of calculating BRV
• Upscaled top reservoir has the same
distribution as fine scale top reservoir
• Only difference is the grid size  1000 bigger
21/4/2015 42

43. NTG upscaling
• Assign facies 2, 4 and 5 with NTG = 1
• Assign facies 1 and 3 with NTG = 0
• Upscaled NTG = number of sandstone cells
within 1000 cells / 1000 cells
• Therefore, there are NTG values between 0
and 1
• Upscaled facies: if upscaled NTG > 0.5 
sandstone, otherwise it is shale
21/4/2015 43

44. NTG cross section at y=500m
21/4/2015 44
Cross-Section, NTG, y=500m
X
Depth
2 4 6 8 10 12 14 16 18 20
2
4
6
8
10
12
14
16
18
20
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Upscaled reservoir NTG = 0.8667

45. Porosity upscaling
• Porosity model is upscaled by taking the
arithmetic mean of porosity within the 1000
cells grid
21/4/2015 45

46. Cross-Section, phi, y=500m
X
Depth
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200 0
0.05
0.1
0.15
0.2
0.25
Porosity cross section at 500m
21/4/2015 46
Cross-Section, Por, y=500m
X
Depth
2 4 6 8 10 12 14 16 18 20
2
4
6
8
10
12
14
16
18
20
0
0.05
0.1
0.15
0.2
0.25

47. Porosity cross section at 1000m
21/4/2015 47
Cross-Section, phi, y=1000m
X
Depth
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200 0
0.05
0.1
0.15
0.2
0.25
Cross-Section, Por, y=1000m
X
Depth
2 4 6 8 10 12 14 16 18 20
2
4
6
8
10
12
14
16
18
20
0
0.05
0.1
0.15
0.2
0.25

48. Porosity cross section at 1500m
21/4/2015 48
Cross-Section, phi, y=1500m
X
Depth
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200 0
0.05
0.1
0.15
0.2
0.25
Cross-Section, Por, y=1500m
X
Depth
2 4 6 8 10 12 14 16 18 20
2
4
6
8
10
12
14
16
18
20
0
0.05
0.1
0.15
0.2
0.25

49. Porosity and HPV estimation
• Average reservoir sandstone porosity is
estimated only for cells upscaled as sandstone
• It is the arithmetic average of these porosity
values
• Average reservoir sandstone porosity = 0.07
• HPV is 14MMm3 (estimated by summing up
the pore volume above owc, then multiplying
with cell volume)
21/4/2015 49

50. Conclusions
• Stochastic simulation yields different
realisations for different runs, especially when
executed using only localised data like well
data
• Volume estimations can have a big range and
uncertainty
21/04/2015 50

51. Conclusions
• The purpose of upscaling is to facilitate
dynamic simulation
• It is important to maintain properties like
volumes during upscaling. In this project, pore
volume in hydrocarbon zone is maintained, so
the hydrocarbon reserves remain unchanged
after upscaling
• As a result, reservoir net-to-gross and porosity
are altered
21/4/2015 51

52. Recommendations
• Stochastic simulation can possibly yield a
result with less uncertainty if seismic data is
incorporated together with well data
• Better volume estimations can be conducted
21/4/2015 52

53. Q & A
21/4/2015 53

54. Possible top markers x trend without faults
21/4/2015 54
Top = -0.0049x2 + 1.4461x + 2943.9
R² = 0.9637
2970
2980
2990
3000
3010
3020
3030
3040
3050
3060
0 20 40 60 80 100 120 140 160 180 200
Top(m)
x grid no.
x grid no. vs. top

55. Possible top markers y trend after
removing x trend
21/4/2015 55
Top = 0.0704y - 7.2096
R² = 0.6928
-8
-6
-4
-2
0
2
4
6
8
10
12
0 20 40 60 80 100 120 140 160 180 200
Residualwithoutxtrend(m)
y grid no.
Y grid no. vs. residual without x trend

56. Residuals after removing x and y trends
21/4/2015 56
-4
-3
-2
-1
0
1
2
3
4
5
6
7
0 50 100 150 200
Residual(m)
X grid no.
Residuals sorted in x
-4
-3
-2
-1
0
1
2
3
4
5
6
7
0 50 100 150 200
Residual(m)
Y grid no.
Residuals sorted in y

57. Residuals stochastic simulation
21/4/2015 57
X
Y
Residual map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200 -15
-10
-5
0
5
10
15
20
Stochastic simulation for 791 points (range = 40, sill = 10), then interpolate residuals using
inverse distance technique (with power 2)

58. Top reservoir map after restoring x and y
trends
21/4/2015 58
X
Y
Top reservoir map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
2940
2960
2980
3000
3020
3040
Bulk rock volume = 0.24km3

59. Average sandstone porosity at well
locations
21/4/2015 59
y = 0.0003x + 0.079
R² = 0.6445
0.08
0.09
0.1
0.11
0.12
0.13
0.14
20 40 60 80 100 120 140 160 180
Averagesandstoneporosity(frac)
X grid no.
Average sandstone porosity sorted in x

60. Porosity residuals
21/4/2015 60
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0 50 100 150 200
Porosityresidual(frac)
Y grid no.
Porosity residuals sorted in y
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0 50 100 150 200
Porosityresidual(frac)
X grid no.
Porosity residuals sorted in x
Porosity residuals do not have trends in x and y directions
 Ready for stochastic simulation

61. Porosity residual map
21/4/2015 61
Stochastic simulation for 791 points (range = 40, sill = 0.01), then interpolate residuals using
inverse distance technique (with power 2)
X
Y
Porosity Residuals Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
-0.03
-0.02
-0.01
0
0.01
0.02

62. Porosity map
21/4/2015 62
Average sandstone porosity = 0.1015
X
Y
Average Sandstone Porosity Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200 0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13

63. Map of pore volume in hydrocarbon zone
21/4/2015 63
• Pore volume = net volume × porosity
• OIIP =15.3MMm3
X
Y
Pore Volume Map
20 40 60 80 100 120 140 160 180 200
20
40
60
80
100
120
140
160
180
200
200
300
400
500
600
700