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

qq

qq

qq

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

QQ

QQ

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