Classification: Internal Status: Draft

Simulation Challenges with WAG Injection

Presentation at FORCE WAG SeminarStavanger, 18 Mar 2009

Vilgeir DalenSenior Advisor, StatoilHydro

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Outline

• Introduction

• High-res simulation

• Field-scale simulation

• Concluding remarks

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General

• The most important results of WAG simulations:

– (incremental) oil production

– gas retention (because of the impact on gas sales and/or gas import)

– location of remaining oil

• Simulation challenges will depend on:

– miscible vs immiscible

– degree of gravity domination

– sand/permeability distribution (e.g. massive vs layered vs labyrinthic)

• Typically, simulation would be done at (at least) two scales:

– high-resolution simulation of segment(s) (2D or 3D)

– low-resolution simulation of entire field (”full-field simulation”)

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Simulated gain by WAG injection

• 7-comp EOS; close to MMP• 2D – 100x80 grid (10mx0.5m)• Homogeneous; except with some barriers handled as transmissibility multipliers (MULTZ)• Including hysteresis on krg

Difference between remaining oil (at std bet) in the grid cells

Difference between remaining oil (at std bet) in the grid cells

WAG vs WI Effect of increasing the gas rate of the WAG

(white means loss)

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Simulated gain by WAG injection

• Same; except with the barriers handled as tight shales with some holes

Difference between remaining oil (at std bet) in the grid cells

Difference between remaining oil (at std bet) in the grid cells

WAG vs WI Effect of increasing the gas rate of the WAG

(white means loss)

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WAG simulation challenges are related to both…

• Reservoir description (ref previous example)

– Size of attics

– Vertical communication (kv/kh, shales)

– Contrasts in horizontal permeability

– Impact of faults on attics, roofs and vertical communication

• Mechanistic parameters

– Relative permeability (3-phase, hysteresis, dep. on surface tension)

– Capillary pressure

– PVT (compositional)

– Diffusion/dispersion

• Resolution

– incl. the assumption of instantaneous equilibrium

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PVT (EOS model)

• 7-8 components is usually a good compromise

• Important to match MMP and miscibility mechanism (usually C/V)

• Multi-contact experiments are considered useful

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What about core to lithofacies scale?

• Laminations could have an impact on trapping of gas and apparent relperm in general.

• Fairly little is known for gas-oil and 3-phase flow – more for water-oil.

SCAL dataCore plug data

Pore scale results

3D Permeability model

Outcrop dataCore data

2 m

3D Lithofacies model

Ripple Planar Trough

Lithofacies scale results

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Establishing high-res model(s)

• What part of the reservoir to pick? How many models?

• 2D or 3D? How large model(s)? Grid?

• How accurate ”boundary conditions” (in a broad sense)?

• Grid-refining a full-field model may retain history matching features

• Geomodel has more hetero-geneities but are they sufficient?

• WAG simulation may require a renewed look into thief zones, shale distribution and baffles for vertical flow from core and log data

Geodata

Geomodel

Simulation model

History-matched simulation model

Upscaling

HM

?

High-res simulation model

?

?

?

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On grids for high-res sector models

• “Optimal” grid resolution for compositional simulation models:

– Lateral resolution ~10 meters; vertical resolution < 1 meter

– For gravity-dominated cases, the sensitivity to the vertical resolution is stronger than the lateral resolution

• Prudhoe Bay Gravity Drainage Miscible Injection Pilot (Waldren, SPE-101455)

– Lateral resolution in pilot area ~10 meters (implemented by LGR)

• WAG Pilot Hassi Berkine South Field (Lo et al., SPE-84076)

– Lateral resolution ~10 meters, vertical resolution 0.375 meter

– Vertical resolution above 1 meter did not match gas breakthrough and saturation profiles

• Ongoing Snorre work: ~1 meter vertically; ~50m horizontally

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Common black-oil alternatives (low-res)

• BO with swelling (variable Rs); possibly with a limited DRSDT which

– reduces the recovery effect of the gas

– capture in a sense limited mixing of the gas and oil in large grid cells

– do not capture the full ”cycle” of miscible flooding

– hysteresis for gas required to capture gas retention

• BO with swelling and vaporization;

– Presently both DRSDT and DRVDT can be specified in ECLIPSE

– Caution required not to vaporize too much oil too fast

– What we see in compositional simulation is that vaporization potential has like an exponential decline

• Should tuning on high-res be through DRSDT (and DRVDT) and/or relperm?

DRSDT = dRs/dt. A value of e.g. 0.1 Sm3/Sm3/day means that it will take at least 1 year for Rs to increase from 100 to 136.5 Sm3/Sm3 in a grid cell.

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Cell 11-20

Cell 61-70

Cell 40

Cell 100

0 2 4 6 8 10 12 140

0.5

1.0

1.5

2.0

2.5

RO

SA

T,

RG

SA

T a

nd

R_

BO

0

50

100

150

200

250

300

350

400

450

500

R_

RS

0

0.0002

0.0004

0.0006

0.0008

0.0010R

_R

V

ONE100_WAG_E100 ROSAT RGSAT R_BO R_RS R_RV

ROSAT 2 RGSAT 2 R_BO 2 R_RS 2 R_RV 2

0 2 4 6 8 10 12 140

0.5

1.0

1.5

2.0

2.5

RO

SA

T,

RG

SA

T a

nd

R_

BO

0

50

100

150

200

250

300

350

400

450

500

R_

RS

0

0.0002

0.0004

0.0006

0.0008

0.0010

R_

RV

ONE100_WAG ROSAT RGSAT R_BO R_RS R_RV

ROSAT 2 RGSAT 2 R_BO 2 R_RS 2 R_RV 2

Black-oil run

Compositional run

Bo

Bo

Rs

Rs

Sg

So

So

Sg

Rv

Cell 40 vs time (years)

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More ”black-oil” alternatives

• Todd-Longstaff (and other miscible options)

– Simple and easy (with the mixing parameter omega in some sense corresponding to DRSDT)

– Originally formulated to capture viscous fingering, but can be looked upon as a simplified representation of a more general ”mixing zone” between virgin oil in front and remaining, stripped oil behind a miscible front/zone.

– Interpolation between miscible and immiscible conditions can be done.

– Could be an alternative for screening studies, but generally not flexible enough for other cases.

• The GI-option; with Rs and Rv correlated with the amount of gas that has flowed through a grid cell.

– Obsolete; not recommended

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Streamline simulation

• Mechanistic high-res simulation in combination with stream-line simulation is an alternative.

• Results will be highly dependent on the type-curves generated from 2D or 3D segments

• May be difficult to distinguish between acceleration and increased recovery effects

• Probably best suited for a fixed, regular well pattern

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Concluding remarks

• Simulation of WAG injection is a real challenge

• A combination of segment (high-res) and full-field simulation is regarded as the best approach

– even if today’s computing power can permit millions of grid cells

– thin layers below shales are especially important if the high-res step is skipped

• For typical NCS WAG injection schemes, geometry and heterogeneities are the largest challenges

• Injectivity and ”smart well” issues may represent additional challenges