1. Simulation Challenges with WAG Injection
Classification: Internal Status: Draft
Simulation Challenges with WAG Injection
Presentation at FORCE WAG Seminar Stavanger, 18 Mar 2009
Vilgeir Dalen Senior 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

6. 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.
SCAL data Core plug data
Pore scale results
3D Permeability model
Core data
Outcrop data
2 m
3D Lithofacies model
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

10. 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 )
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 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 Sm3/Sm3 in a grid cell.

12. Black-oil run Cell 11-20 Cell 40 Cell 40 vs time (years)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

15. 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