1. Jef Caers, Céline Scheidt and Pejman Tahmasebi
Rapid updating of conceptual model, trend and reservoir facies model at the appraisal stage
Jef Caers, Céline Scheidt and
Pejman Tahmasebi

2. Rapid updating Updating at the exploration & appraisal stage
New wells
Improved/new seismic
No production data yet
What is critical ?
Do this fast and transparent
Uncertainty
In this presentation:
Updating of properties (not yet structure)

3. More than just reservoir model updating
Model updating is not just about adjusting the reservoir model(s)
to fit new data
→ this reduces artificially uncertainty
Model updating requires
Verifying whether our current prior assumptions still hold
Updating the beliefs associated with interpretations
Adjusting individual reservoir models

4. Example: facies models built from TIs and trend models
3 possible scenarios (deep channels, lobes and shallow channels)
Deep
channels
Lobes
Shallow
channels
2 vertical trends (absence of trend, presence of trend)
No Trend
Trend Z

5. Overview dnew Inconsistent Interpretation Redo TIs/Trend All new
Models
Incomplete
Interpretation
Add
TIs/Trend
Add
Models
dnew
Global
insight
Confirms
Interpretation
Update P(TI, Tr)
to P(TI,Tr|dnew)
Update
Models
Local update

6. Approach to Problem
30 models per interpretation
 180 models available
Before acquiring new data: existing reservoir models
Data
Interpretations
Well
Seismic
3 TI
2 Trends
New
well data:
dnew
Are the interpretations still valid?
Can we “recycle” some of the models for updating?

7. Use of distances-based scenario modeling
Overview
Inconsistent
Interpretation
Redo
TIs/Trend
All new
Models
Incomplete
Interpretation
Add
TIs/Trend
Add
Models
dnew
Global
insight
Confirms
Interpretation
Update P(TI, Tr)
to P(TI,Tr|dnew)
Update
Models
Use of distances-based scenario modeling
Local update

8. Use of distances-based scenario modeling
Approach to Problem
Synthetic
well values
Existing models
New
well data
Well location
Extract well values from existing models
Distance between wells
Use of distances-based scenario modeling
Update P(Tr, TI|dnew)

9. Probability of TI for a fixed trend and given well data
Sequential Approach
Use of a sequential approach to compute the probability
Trend: provides global information
TI: provides fine-scale details information
P(Tr = ti, TI = sck |data) = P(Tr = ti |data) * P(TI= sck |Tr = ti,data)
Probability of Trend
given well data
Probability of TI for a fixed trend and given well data
Challenges:
Definition of a distance to identify trends
Definition of a distance to identify TIs

10. Distance for Trends
Synthetic
well values
The distance should identify models with similar trends
Must be a function of the facies locations in the well
Use of a Manhattan distance
Prior smoothing of the (indicator) well values is applied
Smoothing mimics better the nature of the trend
Use of moving average -
dij =
No Trend
Trend
No Trend
Trend
P(Tr|dnew)
1.00
0.00

11. Distance for TIs
The distance should identify models with similar scenarios
Patterns in the well can provide information about scenarios
Use of Multiple Point Histogram (MPH)
Analyze of the patterns in the well
Use of JS divergence for the distance
Method of measuring the similarity between two probability distributions

12. Distance for TIs No Trend (100%) Combination of probabilities
Deep channels
Lobes
Shallow channels
Deep Ch.
Lobes
Shallow Ch.
P(TI|Tr = 1, dnew)
0.85
0.15
Combination of probabilities
No Trend
Trend
Deep
Lobes
Shallow
P(TI,Tr|dnew)
0.85
0.15

13. Renewed global insight
Old
global insight
New
global insight 30 30 5
No Trend
35 models for updating
dnew
Trend
No new model is created
Only “local” information at the new well is used
Updating the beliefs associated with interpretations
Adjusting individual reservoir models

14. Methodology
Based on the previous study, 30 lobes and 5 channels models will be used for local model updating
For each model, a region around the well location is updated (i.e. re-simulated)
Use of CCSIM for updating the models (fast!)
For each scenario, one of the “old models” is selected as the pattern database.
The size of updated region is an important aspect which can affect the CPU time and uncertainty space
We will study this aspect

15. Which region size around the well should be used? ?
Simulation Grid
Update Region
Uncertainty space will increase when increasing region
We study the impact on spatial uncertainty of the updated model due to choosing a certain region size
We do not (yet) propose a method for choosing such region

16. Updated Lobes model CPU time=45(s) Model 1 Model 2 Old model Old model
New model
New model
Model 1
Model 2

17. Updated Channel model CPU time=50(s) Model 1 Model 2 Old model
New model
New model
Model 1
Model 2

18. Investigate update using ensemble average (EA)
EA of Lobes Scenario
Excellent conditioning
Due to having larger objects in the channel models, the region size is larger than lobes model.
EA of Channel Scenario
No boundary artifacts

19. Different used update regions
Larger regions increase the CPU time
Do we need to re- simulate the entire grid?
How does the uncertainty space change with different region sizes?
Region 1
< Region 2
< Region 3
< Region 4
< Region 5

20. Combined MDS plot for different resolutions in ANODI analysis
MDS plot representing between updated model variability
Lobes Models
large
Region 5
Region 4
Region 3
Region 2
small
Region 1
The uncertainty spaces are different for each scenario
Uncertainty space for channel model is larger than lobes.
Channel Models

21. Conclusions A rapid model updating in 3 stages
Validation of existing interpretations (global)
Updating of beliefs (global)
Adjusting consist reservoir models (local)
Initial results of a larger project (PROFIT/ENI)
Including process models as TIs
Updating of process model assumptions and parameters
Extending to “new” seismic data