Status and advances within EOR modeling · • Modeling EOR processes requires movement of trapped oil • Quasi static networks are stable but are not designed to consider viscous

CIPR – Center for Integrated Petroleum Research, Bergen, Norway

Status and advances within EOR modeling

Arne Skauge

CIPR, Uni Research

EOR Process modeling

26.05.14 Måltidets Hus


Conclusion - EOR simulation

Detailed mechanistic EOR models can be performed on cross-section models or idealized sector models BUT CARE SHOULD BE USED WITH FULL FIELD MODELS (Grid block size, well controls, etc)

Surfactant Polymer Water Oil

• Surfactant

• Polymer

• Alkaline

• Foam


Simulator

type

Processes

modelled

Degree

of

difficulty

Relative

computing

costs

Amount of

Industrial

Experience

Example

references2

Black Oil

Model

- Primary

depletion

-Waterflooding

-Immiscible gas

injection

- Imbibition

Routine Cheap = 1 - huge

- but there are

still challenges

with upscaling

of large models

- >90% of cases

Any of the books on

reservoir simulation

listed in Section 1.7

Composit-

ional Model

- Gas injection

- gas recycling

-CO2 injection

- WAG

Difficult

Specialised

Expensive

(x3 - x20)

- moderate

- high in certain

companies

Coats, (1980a), Acs et

al (1985), Nolen

(1973), Watts (1986),

Young and Stephenson

(1983).

Composit-

ional Model

- Near Crit.

- gas injection

near crit.

- condensate

development

- MWAG

Difficult Very

expensive

(x5 - x30)

- low to

moderate

as above

Comparison of field experience with various types of simulation model

(after Mattax and Dalton, 1990)


Simulator

type

Processes

modelled

Degree

of

difficulty

Relative

computing

costs

Amount of

Industrial

Experience

Example

references2

Continued

Chemical

Model -

Polymer

- polymer

flooding

- near-well

water shut-off

Not too

difficult

Moderate

(x2 - x5)

- moderate to

large

Bondor et al (1972),

Vela et al (1976),

Sorbie (1991)

Chemical

Model -

Surfactant

- micellar

flooding

-low tension

polymer

flooding

Difficult

Specialisd

Expensive

(x5 - x20)

- low

- mainly

"research type"

pilot floods

Todd and Chase

(1979), Todd et al

(1978), Van Quy and

Labrid (1983); Pope

and Nelson (1978)

Thermal

Model -

Steam

-steam soak

(Huff n' Puff)

- steam

flooding

Not too

difficult

Expensive

(x3 - x10)

- moderate

- high in limited

geographical

areas

Coats (1978),

Prats (1982),

Mathews (1983)

Thermal

Model -

In Situ

Combustion

- in situ

combustion

processes

Very

difficult

Very

specialised

Expensive

(x10 - x40)

- very low Crookston et al (1979),

Youngren (1980),

Coats (1980b)

after Mattax and Dalton, 1990


2006

New processes



Eclipse Low Salinity Model

• Two sets of relative permeability curves.

Hri

Lriri kFkFk 11 1

Hml

Lmlml SFSFS ,1,1, 1

cowH

cowL

cow PFPFP )1( 22


Oil Recovery Berea Core 8 - syntetic brine waterflood

0

10

20

30

40

50

60

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

Volume Injected [PV]

Oil

Re

cov

ery

[%

]

Experimental result

UTCHEM Match

Oil Recovery Berea Core 2 Low Salinity Injection

0

10

20

30

40

50

60

70

0 1 2 3 4 5

PV injected

Oil

Re

cov

ery

[%

]

Experimental result

UTCHEM

Relative permeability curves

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Water saturation

Relat

ive pe

rmea

bility

Initial

Wettability altered

OILWATER

HS LS


Sensitivity tests on the rel perm

F1 - factor

Salt concentration (g/cc)

Hri

Lriri kFkFk 11 1

Dependency on local salt concentration



Well

block

Near

well

Center

part

Near

well

Well

block

Standard 1 10 98 10 1

Coarse grid 1 5 14 5 1

0

0.002

0.004

0.006

0.008

0.01

0.012

0 2 4 6 8 10 12 14 16 18 20

PV injected

Na

+ (

gr/c

c)

Experimental data

Model results (ECL)

Physical dispersion – numerical dispersion


Dispersitivity


Nano particle EOR

Example another new process


Estimation LPS flow functions from by history matching core floods

• Matching waterflood response (Sendra) • LPS match by ECL-200

0

5

10

15

20

25

30

35

40

0 1000 2000 3000 4000 5000 6000 7000 8000

Time

Delt

a P

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Oil P

rod

ucti

on

Core OP Matched DP Core DP Matched OP


Estimation LPS flow functions from by history matching core floods

• Estimated rel perms before and after LPS

Increased oil perm

Reduced water perm


Network Model Challenges

• In traditional Network Model, residual oil at the end of imbibition can not be mobilized

• Modeling EOR processes requires movement of trapped oil

• Quasi static networks are stable but are not designed to consider viscous forces

• Dynamic networks are potentially a good alternative for EOR processes but their estimation of fluid flow is not reliable enough


Our Approach

• Our approach includes Double displacement of fluids to mobilize trapped oil

• It also includes the combination of invasion percolation model with one-phase dynamic network model.

• The dynamic network is necessary because efficiency of many EOR processes like polymer injection, surfactant and etc depend strongly on concentration. The concentration should be evaluated in dynamic basis.


Relperm derived from core flow data and network models


Some Results of LPS injection in a network model

0.25

0.3

0.35

0.4

0.45

0 5 10 15 20

LPS Slug Number [-]

So

r [-

]

Sor

Parameter Value Unit

Network Size 15x15x15 Node

Pore Size 2.4 - 20 μm

Coordination Number 4.02 -

Pore-throat Length 0.8 - 1.12 mm

Porosity 31.02 %

Absolute Permeability 924 mD

Interfacial Tension 41 mN/m

Initial Contact Angle 0.8 - 1.0 Cos( θ )

Wettability Class (After Ageing) Mixed Wet

Large -

Oil Wet Contact Angle (After

Ageing) (-0.2) - (-0.8) Cos(θ)

Water Wet Contact Angle (After

Ageing) 0.1 - 0.3 Cos(θ)

Largest Water Radius/Smallest

Oil Radius (Rwet) 15 μm

MWL


Possibility for visualisation of blocking and oil mobilisation

• Strongly water-wet pores – Driving force for spontaneous imbibition in the smallest pores

• Weakly oil-wet pores give low entry pressure for displacement of oil by water

0

100

200

300

400

500

600

700

3.3

4.2

5.1

5.9

6.8

7.7

8.6

9.5

10.3

11.2

12.1

13.0

13.8

14.7

15.6

16.5

17.4

18.2

19.1

20.0

radius [μm]

bo

nd

s [

-]

PSD

Blocked

Water- wet Oil- wet

0

100

200

300

400

500

600

700

3.3

4.2

5.1

5.9

6.8

7.7

8.6

9.5

10.3

11.2

12.1

13.0

13.8

14.7

15.6

16.5

17.4

18.2

19.1

20.0

radius [μm]

bo

nd

s [

-]

PSD

LPS start

LPS endWater- wet Oil- wet

MWL


other issues


The type of reservoir simulation that becomes more possible with parallel processing. Ex. fine grid and megacell simulation which identifies the scale of remaining oil in a reservoir displacement process; (Dogru, SPE57907, 2000).



+ polymer injectivity

Polymer flooding





Five-Spot Areal Sweep...


Viscous Fingering in a Quarter Five-spot Model, Mo = 17


Theory and Background Mobility Ratio

(Habermann 1960)


Waterflood at adverse mobility ratio (heavy oil)

Skauge et al., SPE 154292



Development of viscous fingers

• Heterogeneity

• Oil Viscosity

• Capillary Pressure

• Relative Permeability

• Rate

• Finer Grid

• Higher Order Flux


CO2 sequestration


Dissolution


SCALES



FOAM Simulations

The sensitivity of the simulated foam response

to variations in the foam parameters was studied:

· Surfactant adsorption on the rock (+ reversability)

· Critical surfactant conc. to generate reference foam strength

· Foam drying effect

· Foam strength - MRF

MRF as distance from injector Gas mobility reduction by foam in the top S1 layer

Modelling of FAWAG on Snorre


Best history match

The best match of the first surfactant slug was achieved with 50 MRF

foam and 100 MRF foam for the second surfactant slug, when foam

drying effect was switched off

Skauge et al., SPE 75157

No foam

MRF=100

WAG Foam


0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13PV injected

Oil

Reco

very

[%]

Experimental Data Best Fit LS-S flood on Core B2

Experimental results: Alagic and Skauge, 2009 Simulation results: Skauge, Ghorbani, Delshad, 2011

Low salinity + surfactant Hybrid processes


S6-S7:4-weeks aged Berea cores

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25

Volume Injected (PV)

Oil

pro

du

ctio

n (

% O

OIP

)

Low Sal 300 ppm polymer

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13PV injected

Oil

Re

cov

ery

[%

]

Experimental Data Best Fit LS-S flood on Core B2

Experimental results: Alagic and Skauge, 2009 Simulation results: Skauge, Ghorbani, Delshad, 2011

Low salinity in combination with other EOR processes

Shiran and Skauge, 2012

Low salinity + surfactant Low salinity + polymer


Final comments

We have a lot of mechanistic tools to model EOR processes Example: MEOR – key mechanisms uncertain, but lower IFT, build up of viscous phases etc., are available FOAM – lot of mechanisms available for history matching, but frontal advance uncertain Surfactant, ASP, detailed mechanisms available, but upscaling is often difficult Low salinity – mechanisms uncertain, but modeling tools seems adequate for history matching upscaling may be influenced by grid resolution (numerical dispersion) Polymer – mature EOR process, but still some issues to be further developed influence of viscoelasticity, injectivity, etc. (unstable waterflood has to be modelled correctly prior to polymer injection Miscible gas – errors often done on extrapolations towards miscibility WAG – combined compositional effect and hysteresis yet to be jointly included in commercial codes


Further comments Too simplified models may not show the potential of the EOR process Known and important physical mechanisms should be respected in the simulation Simulations should be made on controllable models and avoid large grid blocks Cross-sections and/or sector models including key-process EOR mechanisms is the best way of quantifying the potential


Documents

Status and advances within EOR modeling · • Modeling EOR processes requires movement of trapped oil • Quasi static networks are stable but are not designed to consider viscous