developing eor screening methodologiesc196108.r8.cf1.rackcdn.com/maggiethompson.pdf ·...

Developing EOR Screening Methodologies


The Maestro Methodology

� Provides an efficient framework for the selection and rankingof candidate fields for a range of enhanced oil recoveryprocesses.

�Analytical and Numerical Tool/s

�Systematic procedure

�EOR expertise

�Field knowledge and expertise


Maestro Development

Developed by Subsurface group at Winfrith Dorset (Currently theSpecialist Reservoir Engineering Group of RPS)

� 1970s � 1990s � Extensive research into EOR projects, theoreticaland experimental

� 1989 � Development of METEOR I � DTI/NPD� 1989 � 1992 EOR Screening of all UKCS oil fields � 100%success rate in identifying successful UKCS gas injection projects

� 1996 � MAESTRO� developed to enable commercial projects tobe undertaken

� 2000 � 2002 � MAESTRO� re-development �PerformanceIndicators� � �Rapid Simulation�

� 2010 � 2011 � Review of methodology in light of new EORchemicals / processes. Collaboration with BP Institute, Cambridge.


Typical Project Objectives

� Investigate potential for enhanced recovery using gas,chemical or thermal methods

� Rank performance of different displacement processes /reservoirs

� Obtain an indication of economic viability� First pass optimisation studies of best performing processesprior to more detailed modelling


FieldPortfolio

AnalyticalScreening

DetailedAppraisal

ScreeningSimulations

BinaryScreening

EORProject

Hours

Days

Weeks

Months

Minutes

100

10

1

1000

Effort / Res. ReservoirsField

PortfolioBinary

Screening

EOR Screening Methodology


Succ.

0.1

1

10

100

1000

10000

0 50 100 150 200 250 300 350

Vis

cosi

ty, c

p

CO2 miscCO2 immiscHC miscHC immiscN2 miscN2 immiscPolymerCombSteam

Reservoir Temperature, °F

Oil

Visc

osity

, cp

Gas

Chemical

Thermal

EOR Projects (Oil & Gas Journal)


ALL

0

10

20

30

40

50

60

0 2000 4000 6000 8000 10000 12000 14000 16000

Gra

vity

, API


Reservoir Depth, feet

Oil

Gra

vity

, API

Gas

Chemical

Thermal



ALL

0

10

20

30

40

50

60

0 2000 4000 6000 8000 10000 12000 14000 16000

Gra

vity

, API



Oil

Gra

vity

, API

Gas



Binary Screening Criteria Developments

� Ongoing developments meanthat constraints are beingrelaxed� Existing technology� Emerging technology

� Binary screening switchesabruptly from PASS to FAIL atlimits� Fuzzy screening criteria givesa PASS score which variessmoothly from 0 to 1

0

0.2

0.4

0.6

0.8

1

1.2

0 50 100 150 200 250 300 350 400

Temperature (F)

Scor

e PASS

ExistingTechnology

PASS

EmergingTechnology

0

0.2

0.4

0.6

0.8

1

1.2

0 50 100 150 200 250 300 350 400

Temperature (F)

Scor

e PASS

ExistingTechnology

PASS

EmergingTechnology


ALL

0

10

20

30

40

50

60

0 2000 4000 6000 8000 10000 12000 14000 16000

Gra

vity

, API



Oil

Gra

vity

, API

Gas



FieldPortfolio

Maestro

RapidSimulation

PerformanceIndicators

AnalyticalScreening

DetailedAppraisal


BinaryScreening

EORProject



Analytical Screening

� Key performance indicators� Viscous/gravity ratios

� Stone�s, Crane�s, Dietz etc

� Displacement Efficiency� Microscopic displacement

� Buckley-Leverett theory� MMP correlations

� Volumetric sweep� Areal sweep� Vertical sweep� Sweepable volume

Volumetric Sweep

Microscopic Displacement

Distance

Satu

ratio

n

Distance

Satu

ratio

n


Assessing Viability

� Stability� Estimated incrementalrecovery (cf waterflood)� Displacement Efficienciescombine to estimateIncremental Recovery

� Performance Indicators� Economic Indicators� Sensitivity to uncertainty


Optimisation and Economic Indicators - MiscibleGas Injection� Economic Indicators calculated ie gas requirements perincremental barrel

� Optimisation of gas processes

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

0.0 1000.0 2000.0 3000.0 4000.0 5000.0 6000.0

P (psia)

Equi

vale

nt G

as R

equi

rem

ent (

Msc

f/stb

) at

diff

eren

t gas

pric

e ra

tios

11.5234

Priceratio

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0.0 1000.0 2000.0 3000.0 4000.0 5000.0 6000.0

P (psia)

Enric

hmen

t fac

tor

EF

Higher enrichment at lowerpressures

Less gas required at lowerpressures

So operate at lower pressure / higher enrichment

Operational policy is independent of NGL price.


Matching Model to Expected Waterflood Behaviour

� Waterflood recovery factor estimate available fromsimulation model / forecasts

� Analytical model should be calibrated to waterflood estimateRecovery Factor Contours

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Microscopic Displacement Efficiency

Volu

met

ric S

wee

p Ef

ficie

ncy 0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1


Recovery Factor Contours

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Microscopic Displacement Efficiency

Volu

met

ric S

wee

p Ef

ficie

ncy 0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Matching EOR Process to Remaining Oil


FieldPortfolio

RapidSimulation

PerformanceIndicators

AnalyticalScreening

DetailedAppraisal


BinaryScreening

EORProject

EOR Screening MethodologyMaestro


Assess and Quantify Viability

� Include lateral and vertical heterogeneity� Include new well technologies� Generate production and injection profiles (Input toeconomics)

� Sensitivities to identify/confirm critical data� First pass optimisation


Grid Refinement(e.g. 3 x 3 x 1 compared

with full field model)

Screening Simulation Model(s)


0

20

40

60

80

100

120

140

160

180

200

0 1000 2000

Permeability (md)

0

20

40

60

80

100

120

140

160

0 100 200

Permeability (md)

0

20

40

60

80

100

120

140

160

180

0 20 40

Permeability (md)

0

20

40

60

80

100

120

140

160

0 10 20

Permeability (md)

Example Permeability Profiles


Viability of WAG Displacements

NecessaryPartial overrideTotal overrideSimulationScreening

Takes no accountof the position ofthe highpermeability layers

Segregation of gas/ water

Segregation of gas/ water

AnalyticalScreening

Does not considerverticalheterogeneity

Score = 1Score = 1BinaryScreening

CommentField 3Field 1


High heterogeneity (VDP 0.8)and high vertical permeability

Moderate heterogeneity (VDP 0.6)and moderate vertical permeability

Gas Saturation

Sensitivity of Miscible CO2 WAG to ReservoirProperties


Hydrocarbon gas CO2

Gas Saturation

Sensitivity of WAG to Injection Gas


FieldPortfolio

AnalyticalScreening

DetailedAppraisal


BinaryScreening

EORProject



Developing Analytical/Simulation Screening

� Chemical flooding (Surfactant/Polymer)� Wider range of applicability ( ie new chemicals stable at moreextreme conditions) eg high temperatures, low permeability,high viscosity, high salinity

� New flow mechanisms, eg Bright water, Low molecular weightpolymers

� New processes (ASP, Foams, low salinity waterflood)

� WAG� Improving accuracy of analytical methods� Effect of heterogeneity (High/Low K layers / Baffles)


Hele-Shaw Cell Experiments � BP InstituteCambridge


Miscible Flood with High Permeability Streak �HM0014

Hele-Shaw Cell Screening Simulation


Summary

� Maestro is an established methodology providing an efficientframework for selection and ranking of candidate fields forEOR processes by focusing on most promising processes atan early stage

� The methodology continues to be relevant for today�s EORscreening requirements

� New chemicals and processes require developments to themethodology

� Research ongoing to improve analytical methods for WAGprocesses

Thank You


Modelling Flow across Baffles

Flux Pascal�s triangleHele � Shaw experiment

developing eor screening methodologiesc196108.r8.cf1.rackcdn.com/maggiethompson.pdf ·...

Documents