Simulation Challenges with WAG Injection

1
Simulation Challenges with WAG Injection

• Presentation at FORCE WAG SeminarStavanger, 18
  Mar 2009
• Vilgeir DalenSenior Advisor, StatoilHydro

2
Outline

• Introduction
• High-res simulation
• Field-scale simulation
• Concluding remarks

3
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)

4
Simulated gain by WAG injection
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)

• 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

5
Simulated gain by WAG injection
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)

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

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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

7
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

8
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.

Outcrop data
Core data
Lithofacies scale results
Ripple
Planar
Trough
9
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
?
Upscaling
High-res simulation model
Simulation model
?
?
HM
History-matched 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 lt 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

11
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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Black-oil run
Cell 40
Cell 11-20
Bo
Rs
So
Sg
Cell 40 vs time (years)
Compositional run
Cell 61-70
Cell 100
Bo
Rv
Rs
Sg
So
13
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

14
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 todays 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