SPE DISTINGUISHED LECTURER SERIES
SPE DISTINGUISHED LECTURER SERIES
SPE DISTINGUISHED LECTURER SERIES
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<strong>SPE</strong> <strong>DISTINGUISHED</strong> <strong>LECTURER</strong> <strong>SERIES</strong><br />
is funded principally<br />
through a grant of the<br />
<strong>SPE</strong> FOUNDATION<br />
The Society gratefully acknowledges<br />
those companies that support the program<br />
by allowing their professionals<br />
to participate as Lecturers.<br />
And special thanks to The American Institute of Mining, Metallurgical,<br />
and Petroleum Engineers (AIME) for their contribution to the program.
Integrated Reservoir Modeling<br />
Challenges and Solutions<br />
Mohan Kelkar<br />
The University of Tulsa
�� Background<br />
�� Approach<br />
Outline<br />
– Hierarchical Descriptions<br />
– Dynamic Data Integration<br />
– Ranking<br />
– Upscaling<br />
– History matching of multiple descriptions<br />
– Uncertainty representation<br />
Uncertainty representation<br />
�� Future Challenges<br />
�� Conclusions
Background<br />
�� What is integrated reservoir modeling?<br />
Integration of various qualities and<br />
quantities of data to generate inter well<br />
reservoir properties of interest so that<br />
uncertainties in future reservoir<br />
performance can be predicted.
Background<br />
�� What are the challenges?<br />
– Scale and resolution of input and output<br />
– Size of geomodel vs. size of simulation model<br />
– Quantification of Uncertainties<br />
– Solutions of inverse problem, especially during<br />
history matching of production data
�� Drawbacks related to<br />
Conventional History<br />
Matching<br />
– Geological and<br />
geophysical<br />
uncertainties<br />
– Uncertainties in future<br />
performance.<br />
– The relationship<br />
between scale and<br />
uncertainty.<br />
Background<br />
�� Drawbacks related to<br />
Automatic History<br />
Matching<br />
– Computationally<br />
intensive<br />
– Customization to<br />
appropriate input<br />
parameters<br />
– Objective Function<br />
– Initial Guess<br />
Dependent
Structural<br />
Modeling<br />
ell Logs Generation<br />
(Rock Type, Perm)<br />
Spatial<br />
Modeling<br />
Approach<br />
Work Flow<br />
Hierarchical Realizations<br />
Property Modeling<br />
Seismic Porosity Integration<br />
Limited Dynamic Data Integration<br />
Fluid in Place Calculations<br />
Ranking of<br />
Realizations<br />
3 Selected<br />
Realizations<br />
Upscaling<br />
Of Prop.<br />
Selective<br />
History<br />
Matching
Topics of Concentration<br />
�� Hierarchical Descriptions<br />
�� Well Test Integration<br />
�� Upscaling using dynamic<br />
characteristics<br />
�� Objective history matching<br />
�� Future uncertainties representation
Hierarchical Multiple Realizations<br />
�� Rank the uncertain parameters from the largest to<br />
the smallest scale<br />
�� Discretize the range of uncertainties if possible<br />
�� Use fewer number of realizations for small scale<br />
uncertainties<br />
�� Limit the potential number of realizations to less<br />
than hundred<br />
�� Use methods such as experimental design to<br />
efficiently sample the range of uncertainties in<br />
input parameters
Limited Dynamic Data Integration<br />
�� Well Test Data<br />
– Adjustment of fine scale permeabilities through<br />
adjustment factors accounting for fractures,<br />
multi-phase and scale<br />
�� PLT (Production Log Testing) Data<br />
– Vertical adjustment to account for flow<br />
– Determination of fracture conductivity
Well Test<br />
Matching Permeability<br />
Procedure<br />
Re<br />
KH - Well Test Match ?<br />
KH – Sim<br />
Fracture<br />
Stop<br />
Yes<br />
No<br />
Enhanced<br />
Permeability<br />
Simulated Fine Scale<br />
Permeability Distribution<br />
Radial<br />
Upscaling
Alteration without Fractures<br />
�� Calculate the upscaled value of kh from fine<br />
scale description<br />
�� Calculate the ratio of (kh) ( kh) well test to (kh) ( kh) upscale. upscale<br />
�� Interpolate the ratio across the field using<br />
kriging or similar technique<br />
�� Adjust the fine scale permeability value<br />
accordingly
Matching Permeability<br />
Background Enhancement<br />
�� Definition :<br />
– Enhancement required to match well test when<br />
there is no fracture.<br />
�� Physical Interpretation :<br />
– Enhancement required due to micro<br />
fracture/fissures which are not captured by<br />
seismic curvature analysis
Matching Permeability<br />
Log (EF) vs Fracture Density<br />
Background Enhancement
Layer 35<br />
Not-enhanced<br />
Permeability before and after<br />
enhancement<br />
Layer 35<br />
Enhanced
Layer 35<br />
Enhanced<br />
Permeability before and after<br />
enhancement<br />
Layer 35<br />
Enhanced
Permeability Anisotropy
Permeability Anisotropy<br />
�� �� �� �� Assume Assume that that permeability permeability in in the the direction direction of of fractures fractures is<br />
is<br />
maximum maximum permeability permeability and and the the one one perpendicular perpendicular to to that that is<br />
is<br />
the the minimum minimum permeability. permeability. Minimum Minimum value value is is the the base<br />
base<br />
value.<br />
value.<br />
�� �� �� �� The The enhanced enhanced permeability permeability is is calculated calculated as:<br />
as:<br />
�� �� �� �� Based Based on on tensor tensor relationship<br />
relationship
Dynamic Ranking<br />
�� Use the information from all the realizations<br />
�� Use different methods to rank realizations<br />
– Permeability connectivity<br />
– Streamline simulation<br />
– Finite difference – simplified simulation<br />
– Use observed parameter of interest<br />
�� Select three to five realizations for history<br />
matching
Normalized Sweep<br />
1.30<br />
1.20<br />
1.10<br />
1.00<br />
0.90<br />
0.80<br />
0.70<br />
Dynamic Ranking<br />
Realization 15<br />
Realization 41<br />
0.70 0.80<br />
0.90 1.00 1.10 1.20 1.30<br />
Normalized STOIIP<br />
Realization 4
Upscaling<br />
Technique<br />
Upscaling<br />
Vertical Upscaling Optimization -<br />
Procedure<br />
Fine Scaled Model<br />
Upscaled Model<br />
Streamline<br />
Simulator<br />
Further<br />
Upscaling<br />
Select<br />
Prev.<br />
Level<br />
Fine Scaled<br />
Flow Behavior<br />
No Yes<br />
Similar ?<br />
Upscaled<br />
Flow Behavior
Upscaling Scenarios<br />
�� Coarsen the geo-cellular grids while preserving the<br />
necessary level of heterogeneity<br />
– Use streamline simulator to calculate the sweep efficiency of<br />
each vertical layer<br />
– Combine vertical layers having similar displacements<br />
�� Test the vertical upscaling scenarios with Streamline<br />
simulator<br />
– Sweep efficiency of each vertical layer of the upscaled model<br />
should be close to the sweep efficiency of the fine scale model.<br />
should be close to the sweep efficiency of the fine scale model.<br />
�� Fine tune the scenarios if needed
Fine Scale<br />
(93 layers)<br />
Coarse Scale<br />
(66 layers)<br />
Optimum Upscaling Level
Fine Scale<br />
(93 layers)<br />
Coarse Scale<br />
(50 layers)<br />
Optimum Upscaling Level
Fine Scale<br />
(93 layers)<br />
Coarse Scale<br />
(30 layers)<br />
Optimum Upscaling Level
Fine Scale<br />
(93 layers)<br />
Coarse Scale<br />
(20 layers)<br />
Optimum Upscaling Level
Sweep Efficiency, %<br />
Upscaling Optimization<br />
246 Layers 100 Layers 75 Layers 55 Layers 46 Layers 31 Layers<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
0 50 100 150 200 250<br />
Layer Number
Rock Type Upscaling<br />
246 Layers 57 Layers
Porosity Upscaling<br />
246 Layers 57 Layers
Permeability Upscaling<br />
246 Layers 57 Layers (Kx)
Compare Well Logs<br />
Before and After Upscaling<br />
Optimum Level Fine Scale
History Matching<br />
�� Define objective standards for history<br />
matching<br />
�� Vary dynamic parameters within the range<br />
of uncertainty<br />
�� Explore the impact of input parameters on<br />
observed performance<br />
�� Simultaneously history match multiple<br />
realizations
Field Example 1<br />
�� Carbonate, naturally fractured reservoir<br />
�� Influence of water influx as well as injected<br />
water<br />
�� Approximately 55 well strings<br />
�� Parameters adjusted:<br />
– Relative permeability parameters<br />
– Aquifer strength<br />
– Local permeabilities at three wells
Static<br />
Model<br />
Stage – 2<br />
Stage – 1 Stage – 3<br />
Original Model<br />
Field Study<br />
History Match<br />
38%<br />
59%<br />
Well Testing<br />
Calibrated<br />
Model<br />
95%<br />
History Matching<br />
Final Model
Field-wide<br />
Match<br />
Stage-1 (38%)<br />
Stage-2 (59%) Stage-3 (95%)<br />
Cum.Oil<br />
Pressure<br />
Oil Rate Water Cut<br />
Cum.Oil<br />
Oil Rate<br />
Pressure<br />
Water Cut<br />
Cum.Oil<br />
Pressure<br />
Oil Rate Water Cut
Cum.Oil<br />
Jan 1983 Jan 2004<br />
Oil Rate<br />
Stage-1 (38%)<br />
Reservoir Pressure<br />
Water Cut
Cum.Oil<br />
Jan 1983 Jan 2004<br />
Oil Rate<br />
Stage-2 (59%)<br />
Reservoir Pressure<br />
Water Cut
Cum.Oil<br />
Jan 1983 Jan 2004<br />
Oil Rate<br />
Stage-3 (95%)<br />
Reservoir Pressure<br />
Water Cut
BHFP<br />
Oil Rate<br />
Field Study<br />
History Match (cont’d)<br />
Middle Zone Matrix Well<br />
Simulation<br />
RFT<br />
U1<br />
U2<br />
U3<br />
BHCIP/PBU<br />
Model<br />
96 %<br />
Water Cut<br />
PLT<br />
100 %
BHFP<br />
Oil Rate<br />
Field Study<br />
History Match (cont’d)<br />
Upper Zone Fracture Well<br />
BHCIP/PBU<br />
Water Cut Cut
Field Study<br />
History Match (cont’d)<br />
�� Blind Tests :<br />
– 7 Newly Drilled Wells<br />
�� 3 Rehorizontalized Wells<br />
�� 4 New Horizontal/High Deviated Wells<br />
– 17 Pressure Observer Well Strings<br />
– 10-months Extended Production Data<br />
�� Results :<br />
– 6 out of 7 well production was successfully simulated<br />
– 15 out 17 pressure observation wells were matched<br />
– Excellent Field-wide performance during the extended period
Field Study<br />
Blind Test at Rehorizontalized Well<br />
BHP BHCIP/PBU<br />
Oil Rate<br />
Water Cut
BHFP<br />
Field Study<br />
Blind Test at New Fractured Well<br />
BHFP<br />
Oil Rate<br />
Oil Rate<br />
Fracture Realization: 1000 m<br />
BHCIP/PBU<br />
BHCIP/PBU<br />
Water Cut<br />
Water Cut<br />
Well-43<br />
New Well
540<br />
530<br />
2<br />
8<br />
14<br />
E<br />
12<br />
25<br />
23<br />
16<br />
520<br />
780 790<br />
B<br />
Field Study<br />
Saturation Comparison at New Wells<br />
2002/2003 “OH Log SW ” Match Map<br />
19<br />
26<br />
15<br />
4<br />
5<br />
10<br />
11<br />
27<br />
13<br />
H<br />
6<br />
21<br />
22<br />
17<br />
OWC 4055<br />
24<br />
18<br />
1<br />
20<br />
J<br />
7<br />
F<br />
3<br />
9<br />
“R 2+R3”
Field Study<br />
Pressure Match at Observer Well<br />
BHCIP<br />
Simulation RFT
Cum. Oil<br />
Cum. Oil<br />
Oil Rate<br />
Field Study<br />
Blind Test at the Extended Production<br />
Time Period<br />
Reservoir Pressure Pressure<br />
Water Cut Cut
Field Example 2<br />
�� Highly faulted sandstone reservoir (over 100 faults)<br />
�� Large uncertainty with respect to permeability values<br />
�� More than 110 wells – producers and injectors<br />
�� Weak aquifer drive – high water cut in many wells<br />
�� Parameters adjusted:<br />
– Aquifer strength<br />
– Relative permeability parameters<br />
– Capillary pressure curves<br />
– Fault transmissibilities
FLPR<br />
(stb/day)<br />
FGOR<br />
(mscf/stb)<br />
16000<br />
12000<br />
8000<br />
4000<br />
0<br />
16000<br />
12000<br />
8000<br />
4000<br />
0<br />
History Matching<br />
pessimistic<br />
likely<br />
optimistic<br />
historical<br />
Field Level<br />
0 2000 4000 6000<br />
Time, days<br />
0 2000 4000 6000<br />
Time, days
FOPR<br />
(stb/day)<br />
FWCT<br />
30000<br />
20000<br />
10000<br />
0<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
History Matching<br />
Field Level<br />
0 2000 4000 6000<br />
Time, days<br />
0 2000 4000 6000<br />
Time, days<br />
pessimistic<br />
likely<br />
optimistic<br />
historical
GGPR<br />
mmscf/day<br />
GGPR<br />
mmscf/day<br />
4<br />
3<br />
2<br />
1<br />
0<br />
12<br />
8<br />
4<br />
0<br />
History Matching<br />
Bloque#4<br />
Group Level<br />
pessimistic<br />
likely<br />
optimistic<br />
historical<br />
0 2000 4000 6000<br />
Bloque#5<br />
Time, days<br />
0 2000 4000 6000<br />
Time, days
GOPR<br />
mstb/day<br />
GOPR<br />
mstb/day<br />
4<br />
2<br />
0<br />
4<br />
3<br />
2<br />
1<br />
0<br />
History Matching<br />
Bloque#4<br />
pessimistic<br />
likely<br />
optimistic<br />
historical<br />
0 2000 4000 6000<br />
Bloque#5<br />
Group Level<br />
Time, days<br />
0 2000 4000 6000<br />
Time, days
GWPR<br />
mstb/day<br />
GWPR<br />
mstb/day<br />
12<br />
8<br />
4<br />
0<br />
8<br />
4<br />
0<br />
History Matching<br />
Bloque#4<br />
pessimistic<br />
likely<br />
optimistic<br />
historical<br />
0 2000 4000 6000<br />
Bloque#5<br />
Group Level<br />
Time, days<br />
0 2000 4000 6000<br />
Time, days
WOPR<br />
stb/day<br />
WWPR<br />
stb/day<br />
400<br />
200<br />
0<br />
800<br />
400<br />
0<br />
History Matching<br />
W31<br />
pessimistic<br />
likely<br />
optimistic<br />
historical<br />
0 2000 4000 6000<br />
W31<br />
Well Level<br />
Time, days<br />
0 2000 4000 6000<br />
Time, days
WOPR<br />
stb/day<br />
WWPR<br />
stb/day<br />
2000<br />
1600<br />
1200<br />
800<br />
400<br />
0<br />
3000<br />
2000<br />
1000<br />
0<br />
History Matching<br />
W90<br />
pessimistic<br />
likely<br />
optimistic<br />
historical<br />
0 2000 4000 6000<br />
W90<br />
Well Level<br />
Time, days<br />
0 2000 4000 6000<br />
Time, days
WOPR<br />
stb/day<br />
WWPR<br />
stb/day<br />
1200<br />
800<br />
400<br />
0<br />
3000<br />
2000<br />
1000<br />
0<br />
History Matching<br />
W25<br />
0 2000 4000 6000<br />
W25<br />
Well Level<br />
Time, days<br />
pessimistic<br />
likely<br />
optimistic<br />
historical<br />
0 2000 4000 6000<br />
Time, days
Future Challenges<br />
�� “Right Right Scaling” Scaling of Reservoir Model<br />
– Generate reservoir description consistent with<br />
resolution of production data<br />
– Generate reservoir description consistent with the flow<br />
process in the future<br />
�� Prioritize Observations<br />
– Some observations are more important than others<br />
– Large perturbations have more information content than<br />
small perturbations<br />
– Eliminating large amount of observations prior to<br />
history matching will make the process cleaner and<br />
easier
Future Challenges<br />
�� Uncertainty Quantification in Future<br />
Performance<br />
– Fit for purpose uncertainty quantification<br />
– Quantification during the exploration phase<br />
– Use of uncertainties prediction in future<br />
reservoir management
Conclusions<br />
�� A practical work flow allows an efficient<br />
history matching of multiple reservoir<br />
descriptions<br />
�� Partial integration of dynamic data makes<br />
the history matching more efficient<br />
�� Uncertainties in future performance can be<br />
quantified through multiple reservoir<br />
descriptions
Maintain Local Consistency<br />
among Attributes
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