SPE Distinguished Lecturer Program
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professionals to serve as lecturers
Additional support provided by AIME
Society of Petroleum Engineers
Distinguished Lecturer Program
www.spe.org/dl
1
Let’s Model It!
3D Geoscience Modeling – Implications for
R
Reserves
E
Estimation
ti ti and
d Fi
Field
ld
Development Planning
Doug Peacock
Gaffney, Cline & Associates
Society of Petroleum Engineers
Distinguished Lecturer Program
www.spe.org/dl
Presentation Outline
•
•
•
•
•
•
Development of 3D modeling techniques
Current Problems and Issues
Geoscience to Simulation
Solutions and Best Practices
Future Developments
Summary & Conclusions
3
Why did 3D modeling become such
a commonly
l used
d ttechnique?
h i
?
• It’s
It s 3D – real world is 3D
3D, not 2D
• Consistency of horizons, faults, picks etc into
a single
i l fframework
k
– no more overlapping horizons, strange faults,
unrealistic
li ti reservoir
i compartments
t
t
• Common view of reservoir for all disciplines:
– Shared Earth Model concept
• Can be used as a basis for field activity
– appraisal, FDP, development, and updated
4
Why did 3D modeling become such
a commonly
l used
d ttechnique?
h i
?
• Allows use of geostatistics, facies algorithms
– Evaluate heterogeneity in inter-well areas
– Analyze full range of uncertainty
•
•
•
•
•
More meaningful volumetrics
Dovetails the static / dynamic elements
Allows iterative improvements
It’s addictive
Biggest changes in 3D modeling have been
– Increased speed, detail
– Increased
I
d integration
i
i across di
disciplines
i li
5
Classical Modeling
g Workflow
Well Correlation
Simulation Model
Mapping
Petrophysical
Model
Structural Model
Facies Model
6
Presentation Outline
•
•
•
•
•
•
Development of 3D modeling techniques
Current Problems and Issues
Geoscience to Simulation
Solutions and Best Practices
Future Developments
Summary & Conclusions
7
Gross Rock Volume
• GRV is typically the largest single factor in
STOIIP uncertainty
– Often modeled only in relation to uncertainty in
Top Reservoir
– What about: interpretation, isopachs, depth
conversion, fault presence/position/ throw etc
• It requires more effort to model these
uncertainties so it is easy to neglect them
• Especially important in the early / appraisal
stage of field life when facilities design
(capacity, lifespan) are being considered
8
Structural Issues
Oil W
Water C
Contact
9
Fault Position
Lost
Volume
Oil W
Water C
Contact
10
Fault Angle
g
Oil W
Water C
Contact
11
Number of Faults
Oil W
Water
t Contact
C t t
12
Contacts
13
Problems and Issues
with Modeling Techniques
• Predictions are extrapolative rather than
interpretive
• Stochastic models alone do not utilize the
skill and experience of the geologist
– Statistics ((GSS)) vs. Geology
gy ((Object
j
Model))
• Assumptions usually have a large effect
p
on experiences
p
and
• Results depend
preferences of modeler
– Although
g experienced
p
g
geomodelers understand
the effect of these assumptions
14
Modeling
g Assumptions
p
• Data available is never enough to provide full
understanding of the subsurface
Which
Algorithm?
Seismic
Attributes?
Vertical
e ca
Proportions?
Directional
Variograms?
Stationarity?
Trends?
15
Algorithm
g
Assumptions
p
Which
Al ith ?
Algorithm?
Moving Average
Gaussian Sequential
Simulation
16
Variogram
g
Assumptions
p
• Data sampling is rarely sufficient to well define a
variogram – need to rely on experience, analogy,
seismic
i i d
data,
t ttrial
i l & error?
?
Directional
Variograms?
Weak N-S
Strong NE-SW
17
From the same data set…….
Which Algorithm?
Variogram length
and direction
Trends / Seismic
Data
All models will match the input data; differences come from
the decisions that are made about how to build the model
18
Presentation Outline
•
•
•
•
•
•
Development of 3D modeling techniques
Current Problems and Issues
Geoscience to Simulation
Solutions and Best Practices
Future Developments
Summary & Conclusions
19
Geoscience to Simulation
• Scale issues are still problematic
• Better History Matches may be achieved by
correctly identifying contributing rock and
honoring scale
• Feedback still required but earlier is better
20
Scale Issues
• Better History Matches may be
achieved by correctly identifying
contributing rock and honoring scale
• Scale issues are still problematic
– Relationships
p developed
p at a core or log
g
scale are applied at a grid cell scale
– Log,
Log core,
core geo cell
cell, sim cell
21
Scales of Measurement
Well
Log
Core
Production
Seismic
Geo Model
Missing Scale
Sim Model
10-5
10-4
10-3
10-2
10-1
100
101
102
103
104
Measurement Volume m3
22
log
g φ vs. log
g K at different scales
Grid--cell scale
Grid
Core scale
log (Permeability, mD) at core scale
10000
log (Permeability, mD) at grid-cell scale
10000
1000
1000
100
100
10
10
1
1
0.1
0.1
0 10
0.10
0 13
0.13
0 16
0.16
0 20
0.20
0 25
0.25
0 32
0.32
log (Porosity) at core scale
0 40
0.40
0 10
0.10
0 32 0.40
0 40
0 16 0.20
0.16
0 25 0.32
0 20 0.25
0.13
0
13
log (Porosity) at grid-cell scale
Modified from: Worthington 2004
23
Presentation Outline
•
•
•
•
•
•
Development of 3D modeling techniques
Current Problems and Issues
Geoscience to Simulation
Solutions and Best Practices
Future Developments
Summary & Conclusions
24
Model Problems
•
•
•
•
•
•
•
•
Too big, Too complex
Too long to build
Delivered late
Don’t meet business needs
Diffi lt tto update
Difficult
d t
Difficult to History Match
Homogenously Heterogeneous
Don’tt necessarily give good predictions
Don
25
Definitions
Scenario
• Different structural or
geological concept e.g.
– Fault Configuration
g
– Depositional Setting
Realization
• One of a number of
outputs from stochastic
modeling e.g. Gaussian
Sequential Simulation
26
Scenario Method
1
Low
Best
Scenario A
Scenario B
2
3
…
1
2
High
3
Scenario C
…
1
2
3
…
Realizations
• Scenarios may be variously defined
• Well
W ll suited
it d tto early
l fifield
ld lif
life
• Later field life may require fewer historymatched models
27
Scenario Method
Cum
m Probability
100
90
Probabilistic
80
Deterministic
70
60
50
40
30
20
10
0
0
50
100
150
200
STOIIP
• Uncertainty range often greater between scenarios than
within them
• Risk of under-estimating range of uncertainty
• Modeling hundreds of realizations doesn’t mean that all the
uncertainty has been captured!
28
What makes a g
good model?
• Geologically Reasonable
– Represents
R
t geological
l i l understanding
d t di
– Honors available data
• Allows fast and accurate history match
– Assisted by accurate net pay, honoring scale (K,
Sw vs h)
• Gives good predictions
– Of geology (in new wells, one by one removal)
– Of reservoir p
performance
• Fit for Purpose
29
Fit for Purpose
p
• Meets Business Needs
– e.g. time, budget, resources, technical
• Range
g of Uncertainties
– e.g. for Development Planning, Reserves
• Best Technical Case
– e.g. for well planning
• If simulation is involved,
involved discuss all issues
with reservoir engineer:
– Areal
A l lilimits,
it O
Orientation,
i t ti
C
Cellll Si
Size, L
Layering,
i
Upscaling, Feedback, Key Issues, etc
30
Possible Solutions and Best Practices
• “Top Down” or “First Pass” modeling
– Capture
C t
key
k uncertainties
t i ti with
ith smallll number
b off
simpler models – detail added later
– Supports scenario modeling allowing different
concepts & methods – not just uncertainty
• First Pass / Top Down models allow
– Data Validation, Identify Data Gaps
– Early Results and Early Feedback
– Quantify Main Uncertainties and Risks
– Provide focus for more detailed modeling
• Business requirements define model purpose
31
Simulation Results can be Ambiguous
g
A
B
Oil W
Water C
Contact
Simulation Results indicate that Well A should have less
Pore Volume and Well B should have more pore volume
32
Different Structure
A
Different
interpretation
and/or depth
conversion
B
Simulation Results indicate that Well A should have less
Pore Volume and Well B should have more pore volume
33
Thicker Sand
A
Different net pay
cut-off results in
inclusion of lower
quality sands
B
Simulation Results indicate that Well A should have less
Pore Volume and Well B should have more pore volume
34
Better Properties
p
A
Different property
modeling assumptions
e.g. channel width,
depositional
environment etc
B
Simulation Results indicate that Well A should have less
Pore Volume and Well B should have more pore volume
35
Different Contacts
A
B
Deeper contact
e.g. different
contacts in
different fault
blocks, contact
not observed,
uncertainty on
pressure depth
plots etc
Simulation Results indicate that Well A should have less
Pore Volume and Well B should have more pore volume
36
Move Fault
A
Different
i t
interpretation
t ti
and/or depth
conversion
B
Simulation Results indicate that Well A should have less
Pore Volume and Well B should have more pore volume
37
Staffing
g Issues
•
•
•
•
•
•
Software tools are becoming increasingly complex
Software tools are becoming easier to use
Risk of becoming “Nintendo GeoEngineers”
Specialists required to build a “good”
good model?
Encourage generalists or specialists?
Many large companies do have specialist
geomodelers, with appropriate skills
– i.e. Software, Geology, Geostatistics, Experience ……….
– Dedicated group and outsourced to assets
– Or spread throughout assets
• Companies with limited staff, resources, time?
38
Presentation Outline
•
•
•
•
•
•
Development of 3D modeling techniques
Current Problems and Issues
Geoscience to Simulation
Solutions and Best Practices
Future Developments
Summary & Conclusions
39
Technological Developments
•
•
•
•
Better, faster, easier to use software
Grid cell “arms race”
More integration between data and disciplines
New Methods and Tools
– Grid creation (unstructured, ‘easy gridding’)
– Small scale bedding impact on large scale flow
– Multi-point geostatistics
– Inversion Loops
p
– Discrete Fracture Networks (DFN),Geomechanics
• Increased
c eased use o
of d
digital
g a & ou
outcrop
c op a
analogues,
a ogues,
– Potential for industry/academia collaboration
40
Semi-Va
ariance
Limitations of Traditional Geostatistics
Lag Distance
1
2
3
2-point correlation is not enough to characterize connectivity
Source: Caers
41
Multi-Point Geostatistics
Training Image
• Offers a way of including
more geology
• Training Images required
• Could be based on digital
analogue/outcrop data
• Still depends on
selection of an
appropriate training
image
Final Model
Tetzlaff et al 2005
42
The Future……….
• Effective modeling in the future will require a
gy and p
process
blend of technology
• Technology will continue to evolve
– More detail = better models?
– Multi-point statistics, better use of seismic, better
workflows automated history match
workflows,
match, no
upscaling, etc ………….
• Process and smarter working practices may
deliver greater benefits
– Better understanding of uncertainty
– Fit for Purpose models
43
The Future ?
Easy Gridding
Input
Validation
Geological
G
l i l
Model
Inversion
Loops
Validation
Automatic
History Match
Input
MPS / Digital
Analog
44
Summary & Conclusions
• 3D Geoscience modeling will continue to be a
widely used and indispensible technique
• Modeling techniques have implicit assumptions built
in to them
– Know
K
what
h t th
they are and
d what
h t affect
ff t they
th have
h
• Modeling methods are continuously evolving
– Only expert modelers may be able to keep up to date
• Do not be seduced by “Nintendo GeoEngineering”
• It is still good practice to:
– Think about the purpose of modeling (Fit for Purpose)
– Understand data, data validity, data limitations
– Define main assumptions and continue to challenge them
45
Let’s Model It!
3D Geoscience Modeling – Implications for
Reserves Estimation and Field
Development Planning
Doug Peacock
Gaffney, Cline & Associates
Society of Petroleum Engineers
Distinguished Lecturer Program
www.spe.org/dl
46
References
Worthington, P.F., 2004, The Effect of Scale on the
Petrophysical Estimation of Intergranular Permeability:
Petrophysics, vol 45, no 1
Caers, J., 2002, Stochastic inverse modeling
g under realistic
prior model constraints using multiple-point geostatistics.
Invited presentation for the IAM2002 Workshop on
""Quantifying
Quantifying uncertainty and multiscale phenomena in
subsurface processes, Minneapolis, Minnesota, Jan 7-11
Tetzlaff et al, 2005, Application of multipoint geostatistics to
honor multiple attribute constraints applied to a deepwater
outcrop analog, Tanqua Karoo Basin, South Africa: SEG
Expanded Abstracts vol 24, 1370,
47