1. ADNOC Main Challenges With the Current
Simulation Modeling Workflows
AbuDhabi 2013
ADNOC/Schlumberger Simulation Workshop
5th December 2013
Faisal Al-Jenaibi

2. General Simulation Modeling Areas of Concern
 Static Model:
 Stratigraphic and Layering Framework.
 Petrophysical Parameters Distribution (permeability, porosity, RRT’s ..etc).
 Fluids-in-Place & Water Saturation Modeling.
 Vertical/Lateral Transmissibility across Faults.
 Upscaling Technology.
 Dynamic Model:
 Simulation Model, Size and Resolution.
 Definition of Transition Zone, SCAL framework issues.
 Transition phase between history & prediction modes (VFP’s tables).
 High Permeability Streaks and thin Barriers Intervals.
 Dual Porosity & Dual Permeability Models.
 Upscale/Downscale Sector Model from/to Full Field Model.
 Variable “Sor” per RRT based on Wettability.
 Streamline Technology.
2/20

3. General Simulation Modeling Areas of Concern
 Static Model:
 Stratigraphic and Layering Framework.
 Petrophysical Parameters Distribution (permeability, porosity, RRT’s ..etc).
 Fluids-in-Place & Water Saturation Modeling.  (Part-1)
 Vertical/Lateral Transmissibility across Faults.
 Upscaling Technology.
 Dynamic Model:
 Simulation Model, Size and Resolution.
 Definition of Transition Zone, SCAL framework issues.  (Part-2)
 Transition phase between history & prediction modes (VFP’s tables).
 High Permeability Streaks and thin Barriers Intervals.
 Dual Porosity & Dual Permeability Models.
 Upscale/Downscale Sector Model from/to Full Field Model.
 Variable “Sor” per RRT based on Wettability.
 Streamline Technology.
3/20

4. Part-1: Fluids-in-Place & Water Saturation Modeling
 Distribute of the FIP’s in the static model should be linked with:
 Geological features i.e. (sedimentology, faults, facies, layers pinchout , seismic, ..etc).
 Honor and distribute porosity logs profiles.
 Classification of RRT’s groups (MICP’s, pore throat distribution, ..etc). Plot “PERM-PORO
relationship vs. RRT’s groups”.
 Wells Sw_log is the main reference parameter need to be honored and matched,
well-by-well to ensure appropriate FIP’s estimation.
 End-Point-Scaling approach to be used only with absent of SCAL data.
PORO
PERM
Sw_log
HeightaboveFWL
4/20

5. Part-1: Water Saturation Model
Simple approach to smoothen cells water saturation nearby FWL
Iteration - 00
co-krigging “stochastic” approach used to distribute
Sw_log data in the static model
Iteration - 01
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6. Part-1: Water Saturation Model
Iteration - 00
Iteration - 03
Iteration - 01
Iteration – 06
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7. Part-1: Water Saturation Model
Iteration - 00
Iteration - 03
Iteration - 01
Iteration – 06
7/20

8. Part-2: The Current Height Function “Pc’s Curves” Design
4 Wells, Sw-Logs data
Depth,ft
 The height function curves represent thick transition zone.
 Massive volume of water is mobile at very early time.
8/20

9. Part-2: Height Function “Pc’s Curves” Design
Facts:
• Many wells which reported with high Sw_log data have
produced dry oil during production test although they
were completed nearby water zone.
• High porosity & permeability rock type will have lower
capillarity force i.e. (Pc curve) than low porosity &
permeability rock type.
• Due to high heterogeneity in carbonate reservoir, single
Pc curve per rock type might not be enough to reflect
Sw_log data.
9/20

10. SW
HeightaboveFWLdepth(ft)
0 1
Transition Zone
Oil Zone
Oil Dry Limit
FWL Depth
The Current Pc’s Design
Part-2: Height Function “Pc’s Curves” Design
The Current Kr’s Design
SW
Kr’sCurves
0 1
FWL Depth
SwcrSwirr Sor
1
Swcr
In order to slow down water movement in transition zone, either by use:
(1) Unphysical Swcr’s “Simulator Parameter”
(2) Very low Krw’s values
(3) Unsupported permeability multiplier
Swirr
10/20

11. SW
HeightaboveFWLdepth(ft)
0 1
Water Zone
Transition Zone
Oil Zone
Oil Dry Limit
FWL Depth
The Current Pc’s Design
SW
HeightaboveFWLdepth(ft)
0 1
Water Zone
Transition Zone
Oil Zone
High PORO
High PERM
Low PORO
Low PERM
Oil Dry Limit
FWL Depth
The Proposed Pc’s Design
Part-2: Height Function “Pc’s Curves” Design
11/20

12. Part-2: The Proposed Height Function “Pc’s Curves” Design
4 Wells, Sw-Log data
Depth,ft
PC’s Curves Should:
• Address the
thickness of the
transition zone.
• Provide excellent
match with initial
Sw_log data.
• Assist in
achieving better
history match.
• Contribute in
model stability.
• Optimize
saturation tables.
• Eliminate Swcr’s
usage.
• Address
wettability issues.
12/20

13. Part-2: Dynamic Model Initialization, Case Study-Aug
Static Model “Sw_log” Dynamic Model “Sw_pc”
Co-krigging “stochastic” approach
used to distribute Sw_log data in the
static model
Generate 12 drainage Pc’s curves to
replicate Sw_log data into dynamic
model
Sw_log vs. Sw_pc
Excellent replication of “Sw” static model in the dynamic model has been achieved following
applied ADNOC the proposed new Pc’s curves design.
13/20

14. Part-2: Dynamic Model Initialization, Case Study-Aug
Static Model
“Sw_log”
Dynamic Model
“Sw_pc”
Water Saturation
Cross-Section
14/20

15. Part-2: Dynamic Model Initialization, Case Study-Aug
Pc’s Curves Examples
Best RRT Intermediate RRT Tight RRT
A total of 194 saturation
tables were used in the
Current dynamic model
A total of 24 saturation
tables were used in the
updated dynamic model:
12 Drainage Pc’s
12 Imbibition Pc’s
15/20

16. 16/20

17. 17/20

18. Part-2: History Match , Case Study-Nov
18/20
Following to implement ADNOC workflow to design Pc’s curves in 2 weeks time
frame, massive field GOR and WCT were enhanced.

19. Conclusions
 The current technical challenges and concern issues, which are related to
modeling activities, are subject for farther integrated workflows that are
requiring very promising technologies and powerful tools in order to address
them in batter and practical ways.
 The proposed Pc’s curves design showed very encourage results with respect to
reproduce static water saturation model into dynamic model at high quality,
while contribute in model stability and respect more physics.
 Due to the complexity of AbuDhabi reservoirs, with the high uncertainty levels
present in most of them, more resolution models are needed to be constructed
to reflect reservoirs production behaviors in more accurate mode.
 Sharing lessons learned with regard to modeling activities and implemented
workflows is essential to maximize knowledge and experiences exchange,
while moving into close collaboration to overcome technical challenges.
19/20

20. Thank You
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