Streamline-Based Workflows for
Brown Field Flood Management
Streamsim Technologies, Inc. September 2011
Rod Batycky
Outline
• Overview of Streamsim Technologies
• Flood Management • Objectives
• Solutions
• Introduction to Streamlines
• SL-Based Workflows • Surveillance
• Rate Optimization
• Simulation
• History Matching
• Wrap-up
Streamsim Technologies
• Founded in 1997 • Based on research at Stanford University • 1997-2000 development funded by BHP, Elf, & Shell.
• Software Products & Services • 3DSL (streamline simulator) • studioSL (GUI & workflow container) • Training & Consulting
• Company Objectives • Promote SL-simulation technology as a powerful RE
tool. • Lead the industry with innovative, SL-based reservoir
engineering workflows.
The Objective…
With large mature floods, how do engineers easily identify: • Injector patterns? • Efficient use of injected fluids? • Fluid cycling? • Well rate targets? • Infill/conversion locations? • EOR/IOR opportunities?
? Engineering toolbox:
– analogs
– decline analysis
– material balance
– CRM models
– flow simulation
streamlines production surveillance
flow simulation
•Well locations •Prd/Inj history
•Well-pairs •Analysis per injector pattern •Identify good vs poor areas of sweep •Rate targets
•Geomodel •Flow-physics •History matching
•Analysis on a per well-pair basis •Sweep imbalances per producer •Flood optimization via rate changes •Propose & forecast rate changes •Infill locations
Two-Step Solution with Streamlines
Overview of
Streamline Simulation
What is a Streamline?
• A line that is tangential to the local velocity vector. Velocity vector follows local pressure gradientDarcy’s Law
• Streamlines start at sources (injectors) and end at sinks (producers).
• Easy to trace streamlines/streamtubes analytically in 2D homogeneous systems using the stream-function (Bear 1972).
Analytical Streamline Paths
repeated 5-spot
unbalanced rates
repeated 5-spot
balanced rates
Streamlines colored by originating injector
Streamlines in Field Applications
• Heterogeneous rock properties (porosity, perm, NTG, rockregions, …).
• 3 dimensional flow. • Multi-phase flow (immiscible, miscible, polymer). • Changing well conditions. • Gravity effects. • Compressibility. • … • For field applications, modern streamline
simulation (SLS) requires numerical methods.
Steps in SL Simulation
For each time step do:
dvt
Pressure solution on the Eulerian grid
Trace SL’s to create “dynamic” grid
Solve transport along SL-grid (not used in surveillance)
Map from SL’s to grid
(not used in surveillance)
Repeat
Correct for gravity (not used in surveillance)
Modern SLS: Dual Grids
1. Eulerian “static” grid
2. Lagrangian “streamline” grid
11
Areas of applicability of SL’ simulation:
• Flow visualization
• Reservoir surveillance
• Ranking & uncertainty in reservoir performance
• Upscaling
• History Matching
• Advanced EOR (miscible, polymer) processes
• Injection/Production rate targets
RESERVOIR ENGINEERING • Convective dominated displacements
•Waterfloods, Misciblefloods, Strong aquifers
• Production surveillance, HM, full-field flow
modeling
3DSL
FD’s
• Complex flow physics
• Primary production below bubble point
• Gas fields
• Complex well/surface facilities
• Sector models
•“Small” earth models
SL’s are complementary to traditional simulation.
GEOMODELING • Fine scale geology
• Screening/Ranking
• Sensitivity to gridding/geology
• Reference solutions for upscaling
SL’s vs FD – Where to Use
Reservoir Surveillance
SPEREE April ’08 (SPE 95402)
Introduction – What is Surveillance
• Reservoir flood surveillance is the association of produced volumes with injected volumes to identify: • well-pair interactions • pattern recovery • well rate targets • trends in production/injection data
• Analysis of production data based on well-pairs.
• The key issue is: How are well-pairs identified and the produced volumes associated with injected volumes? • Classic method -> define by hand • Streamline method -> based on flow response
Trace Streamlines
Flux Pattern Map
WW2 WW4
33.4005day
rbqSL
Flux Pattern Map
Flux Pattern Map
Well Allocation Factors—Injectors
%8067.99533.4005
33.4005
%20
67.99533.4005
67.995
WW5 WW2
WW4
Well Allocation Factors—Producers
%6233.240433.4005
33.4005
%3833.240433.4005
33.2404
WW2
WW4
WW3
Well-Pairs and Heterogeneity
Surveillance model Detailed Flow Model
Grand Forks UMK Pool
Well-Pairs and Heterogeneity
Surveillance model Detailed Flow Model
Surveillance model Detailed Flow Model
Grand Forks UMK Pool
Well-Pairs and Heterogeneity
Surveillance model Detailed Flow Model
Grand Forks UMK Pool
Streamline-Based Surveillance
• Not interested in the details of individual SL paths.
• Only interested in the total flow of the streamline bundle between each well-pair.
• Bundles are relatively insensitive to small scale geology but are a strong function of well rates.
Flux Pattern Map Streamlines
FPmap – Combine Multiple Timesteps
Time 1
Time 2
Time 3
Cumulative FPmap
• Combine any range of timesteps.
• View “average” patterns.
Bubbles vs FPmap
Cum Water Injection Cum Water Injection
Average Patterns: Cycling vs Efficient
Connections Cum oil prod Connections Cum wat prod
Connections colored by
producers.
Thickness proportional to
cumulative production over
time range (rm3).
Injector Centered Patterns • Flow is not limited to fixed patterns and there can be
significant off-pattern flow. • Why classify this flow as influx/efflux and associate with a
different pattern?
• To quantify the true effectiveness of an injector, associate all of the production it is responsible for.
• Surveillance for improved sweep management vs VRR management.
Time 1 Time 2 Time 3
Phase rate WAF’s – Historical • Associate produced oil with injected water
using historical volumes and the WAF’s. • WAF’s come from the total flow rates between well-pairs
(streamline bundles).
• Back allocated historical volumes (oil, wat, gas) to each injector based on the WAF’s. Assumes same wcut per producer connection.
193
,
1101
,
193
,
272
,
193
,
179
,
%2.34
%9.26
%9.38
HTL
histo
HTL
histo
HTL
histo
HTL
histo
HTL
histo
HTL
histo
Per-producer
WAF’s –> Injector Efficiency
3337
4389
1196979
49.048.0
85.096.0
27.019.0
H
o
H
o
H
o
P
o
P
o
P
o
P
o
QQQ
Off-set oil production due to
injector P9-7:
Per-injector
off-set oil production [rb/day]
water injection [rb/day]effI
The Pattern Efficiency Plot
Pattern Water Injection Rate
Patt
ern
Oil
Pro
du
cti
on
Rate
100% Efficiency
75% Efficiency
50% Efficiency
25% Efficiency
0% Efficiency
• Snap-shot of current performance of patterns
The Conformance Plot
Pattern Water Injection Volume
Patt
ern
Oil
Pro
du
cti
on
Vo
lum
e
tQN P
o
P
o
7979
• Overall performance of patterns
Time
Per Injector Conformance Plot
Time
Conformance Plot for Injector P9-7
Per Producer Conformance Plot
Flood Management (well rate targets with floodOPT)
SPEREE April 2006
Managing Brown Fields Using SL’s
• Identify good/bad patterns using SL’s.
• To impact sweep, increase/ decrease rates at both producers and injectors.
• Goal => Best use of injected volumes for displacement purposes, not re-pressurization.
• “Poor mans” waterflood optimization.
off-set oil production [rb/day]
water injection [rb/day]effI
For each connection determine:
equivalent to “Oil Cut”
New Target Rates (SPEREE 4/06)
)( 89/79
89/7989/79
Iwqq oldnew
x
newnew qq /7979
weighting function
New Injection Rate (sum all connections)
x
newnew qq /6969New Production Rate (sum all connections)
New total rate of connection 9-7/9-8
39
A heuristic workflow rather
than formal optimization.
Wells (voidage) Slines FPmap
Example: Horsefly
• 40+ year waterflood, 98% wcut
Horsefly – Pattern Performance
current efficiency
cumulative efficiency cumulative performance
Horsefly Rate Targets – Scenario 2
Old Injection Rates
New Injection Rates increase rates
Horsefly Rate Targets – Scenario 2
floodOPT Workflow
Capture current state of the flood,
determine which wells to include,
etc…
Apply algorithm and extract new
target rates for injectors AND
producers…
Decide the period of time for which
you want to keep target rates
before updating them…
• If a flow simulation model exits…
Warner - Rate Optimization
• Heavy oil field Saskatchewan – 100+ producers, 20+ injectors. High water production.
• Promote high efficiency connections vs fluid cycling by setting well rate targets.
FPmap at end of HM
- Red: low IE pairs
- Green: high IE pairs
Injector Efficiency
at end of HM
Warner - Rate Optimization
• Maintain field injection rate.
• Update well rates at 1-year intervals.
optimized rates
Field water prd
Field oil prd
SL-Based Flow Simulation
Flow Simulation
• Want to build a predictive model of the reservoir.
• Move fluids forward in time starting from the initial “known” state of the reservoir , Transport Step.
• Relevant physics along each streamline.
• Update the streamlines in time to account for non-linearities (gravity, compressibility, well rate changes)
• Check against historical performance, forecasting…
Transport Along Streamlines
• Efficient method;
• Each streamline is a 1D independent solution.
• Easy to solve 1D problems, parallelizable.
• Take large global timesteps as fluid transport is decoupled from the 3D grid.
• Downside;
• Not mass conservative.
• Limited to convective dominated displacements.
Transport Along Streamlines
Time 1
Time 2
1D Solver for each Streamline
• Complex physics
• Parallel compute
Polymer Flooding (IOR) • Immiscible, compressible phases
• 4 components: oil, water, polymer, salt
• Water viscosity is a function of polymer & salt concentrations, shear rate
• Polymer adsorption
• Permeability reduction due to adsorption
3DSL
ECLIPSE
1D Oil & Water Production
water
oil
51
Polymer – 3D Example
P1 P5
P10
P17
P13
3DSL
ECL
52
OMV – Romanian Waterflood
from SPE 132774
• 40 years waterflood, 100+ wells
• Porosity: 30 %
• Permeability: 700 mD
• Oil density: 974 kg/m³
• Oil viscosity: 100 cp
• Water viscosity 0.5 cp
• Reservoir temperature: 53 °C
• Water salinity: 23000 ppm
• Initial reservoir pressure: 90 bar
history
due to
polymer
Field Oil Rate - Simulated
Extend surveillance metrics to polymer efficiency to identify “best” patterns to flood.
Flow Pattern and Flux Map
Where is the best use of polymer? Depends on...
• Pattern ROIP‘s
• Polymer concentration
• Slug size
Utility/Efficiency Factors
• Polymer injection projects are OPEX driven:
• Per pattern efficiency factor
• Per pattern utility factor
[bbl] produced oil ultimate cumulative
[kg] injectedpolymer mass cumulativeEF
[bbl] produced oil lincrementa cumulative
[kg] injectedpolymer mass cumulativeUF
55
EF - 1
1
2
3
4
5
Cumulative polymer injected in kg
Cum
um
ula
tive O
il P
roduction
from SPE 132774
Pattern Performance Plot
56
1
2
3
4
5
Cumulative polymer injected in kg
Cu
mula
tive o
il pro
duction in b
bl
6
Cumulative polymer injected
Cumulative incremental oil produced
Per Pattern Utility Factor
57
Workflow Using Patterns
1) Improve waterflood – non EOR/IOR
2) Disregard patterns with low EF for
base slug size/concentration
3) Pattern sensitivity to concentration/slug size
4) Pick individual pattern operating conditions
based on UF
5) Full field using optimal individual strategies
C
um
ula
tive
Oil
pro
du
ce
d –
(rm
³)
Cumulative Polymer injected – (kg)
Cu
mu
lati
ve
Oil
pro
du
ce
d –
(rm
³)
Cumulative Polymer injected – (kg)
0
50000
100000
150000
200000
250000
300000
350000
0 500000 1000000 1500000 2000000
Cumulative polymer injected - (kg)
Cu
mu
lati
ve
oil p
rod
uc
ed
- (
rm³)
12 years
9 years
6 years
3 years
Different slug sizes at a fixed concentration
0
50000
100000
150000
200000
250000
300000
0 200000 400000 600000 800000 1000000
Cumulative polymer injected - (kg)
Cu
mu
lati
ve
oil p
rod
uc
ed
- (
rm³)
c. 500kg/m³ s. 6y
c. 750kg/m³ s. 6y
c. 1000kg/m³ s. 6y
c. 1250kg/m³ s. 6y
c. 1500kg/m³ s. 6y
Different concentrations at a fixed slug size
0
0.5
1
1.5
2
2.5
3
3.5
4
0 100000 200000 300000 400000 500000 600000 700000 800000
Incremental oil (bbl)
Uti
lity
Fa
cto
r (k
g/b
bl)
58
Polymer Simulation Results
Cumulative oil production
Cumulative polymer
injected decreased…
…cumulative oil recovery increased
Cumulative polymer injection
from SPE 132774
Base water flood
59
SL-Based Well-Level
History Matching
Field-level Match vs Well-level Match
• Overall match is good. First-order flow effects (geology, physics, well events) captured at the field level.
• Poor well-level match. How to improve the well-level match?
water rate
oil rate
Field Rates Cum Oil per Well
SL-Based HM with Geostat Constraints
• Joint Streamsim/Stanford/Industry Project
• Started 2004, first software release May 2007
SL-Based HM with Geostat Constraints
• To move away from multipliers and head towards geologically consistent history matching by combining geostats and streamline simulation.
• “Production Data Integration” during geomodeling.
Conceptual
Geological Model
P1
P2
P6
P5
P4
P3
I1I2
I3
0.0
0.2
0.4
0.6
0.8
1.0
0 1000 2000 3000 4000
Time (days)
Wa
ter
Cu
t 2 ref
2 match
5 ref
5 match
P1
P2
P6
P5
P4
P3
I1I2
I3
0.0
0.2
0.4
0.6
0.8
0 1000 2000 3000 4000
Time (days)
Wa
ter
Cu
t 2 ref
2 match
5 ref
5 match
P1
P2
P6
P5P4
P3
I1I2
I3
How much and where to correct.
c
kk
old
new
old
c
old
oldnew
c
Qw time mismatch
Correction factor
Determine sl bundle
Map c* to grid
HM Workflow Overview…
Update Prior Model
Static Data Prior model
Iterate
Streamlines
HM ok?
NO
Yes
Judy Creek - SPE108701
• A detailed geological with 1.4 million cells total, 600K cells active.
• A P50 model added 110 MMbbl to STOIIP.
• Primary objective was flow simulation & history matching at the field and well-level for the new model.
• Forecasting and flood optimization is on-going.
Sample C* Maps
• Both the magnitude and location of C*’s vary as:
– Different time steps are used for matching.
– Same time step used but geomodel is repeatedly updated.
Layer
28
Run 30 - 5724 days Run 31 - 6029 days Run 32 - 6029 days
Field Oil Rate Match
Initial run
Final run
Final match after 42 retained permeability updates.
Judy Creek – Sample Well Matches – Oil
Rate
Initial match
Final match
Final Well-level Match
historical
sim
0.0E+00
2.0E+05
4.0E+05
6.0E+05
8.0E+05
1.0E+06
1.2E+06
1.4E+06
1.6E+06
1.8E+06
0.0E+00 2.0E+05 4.0E+05 6.0E+05 8.0E+05 1.0E+06 1.2E+06 1.4E+06 1.6E+06 1.8E+06
Non HM Wells
HM Wells
historical
• Cumulative oil produced at the end of the simulation.
• Approximately 7 man-days over 2 calendar months (50+ flow simulations) required
for well-level history match.
Summary/Conclusions
• Numerous advances in SLS over the past 10 years have resulted in practical workflows for large mature floods.
• Surveillance • Patterns are dynamic, quantifiable, easy to compute. • Injector efficiency and per-pattern conformance plots.
• Well rate targets
• Re-allocate volumes according to well-pairs. • Improve use of injected fluids in existing wells.
• Flow Simulation • Efficient full-field models with EOR/IOR physics.
• History Matching
• Well-level application. • Integrate production data with geomodeling.
• Caution: SL’s not universally applicable.
Further Information…
www.streamsim.com
• Technical papers
• Examples
• Software updates
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