Capstone 5: Heavy-oil reservoir under WAG flood

Heavy-oil reservoirs (oil viscosity 100-10,000 cP at reservoir conditions) cannot be produced by primary depletion alone — viscosity inhibits flow. Secondary recovery via water-flooding leaves substantial oil in place due to unfavorable mobility ratio (M = μ_o/μ_w >> 1). Tertiary methods like WAG (Water-Alternating-Gas) improve sweep by alternately injecting water and gas.

Why WAG works for heavy oil

Gas dissolved in oil reduces viscosity locally (Carmichael correlation: η_oil decreases with dissolved gas). Water-flood pushes oil; gas-injection cycle dissolves into oil and reduces its viscosity ahead of next water-flood cycle. Net effect: improved mobility ratio at the displacement front; reduced viscous fingering; better SWEEP efficiency.

Geostatistical input matters

Sweep efficiency depends strongly on reservoir HETEROGENEITY. High-k streaks short-circuit injection water past low-k zones, bypassing oil. Realisations show:

• Smooth, low-heterogeneity fields → uniform sweep, high recovery
• High-heterogeneity fields → fingering, channelling, lower recovery

The widget below simulates a horizontal sweep from the left edge across the field. Each realisation produces a final oil-saturation map under water-only vs WAG injection. Recovery fractions are computed and compared.

Try it

• Defaults (log-k SD = 0.6, 3 WAG cycles). Compare blue (water) vs green (WAG) histograms. WAG should give ~10-25% higher recovery in P50 — that's the practical advantage. The spread of each colour indicates realisation-to-realisation uncertainty.
• Set log-k SD = 0.2 (low heterogeneity). Recovery distributions tighten dramatically; the WAG-vs-water gain is smaller because sweep is already good. WAG's ADVANTAGE is largest in heterogeneous reservoirs.
• Set log-k SD = 1.2 (high heterogeneity). Realisation spread for both methods widens dramatically. WAG's relative advantage may be similar in % but absolute gain on a per-realisation basis is more variable.
• Crank WAG cycles to 6. Diminishing returns: more cycles ≠ proportionally more recovery. Field optimization picks the cycle count balancing recovery against operational cost.
• Compare the saturation maps for water vs WAG on the same realisation. WAG's map should show LESS BYPASSED OIL — more uniform sweep. Visual confirmation of the bypass-reduction mechanism.

An operator considering WAG for a heavy-oil reservoir asks: "How does the value of more geostatistical realisations compare with the value of a pilot WAG cycle in the field?" Sketch the decision-theoretic argument linking realisation uncertainty to pilot-test design.

What you now know

You can run a full geostatistics → flow-simulation → comparison workflow for heavy-oil EOR. The same machinery extends to other EOR methods (steam flood, chemical flood, ASP) and to 3D reservoir-simulation studies. The key insight: heterogeneity drives sweep uncertainty; EOR methods that mitigate heterogeneity (WAG, polymer, foam) give the biggest gains in heterogeneous reservoirs.

Mark capstone complete →

References

• Christensen, J.R., Stenby, E.H., Skauge, A. (2001). "Review of WAG field experience." SPE Reservoir Evaluation & Engineering 4(2), 97-106.
• Sheng, J.J. (2013). Enhanced Oil Recovery Field Case Studies. Gulf Professional Publishing.
• Lake, L.W., Johns, R.T., Rossen, W.R., Pope, G.A. (2014). Fundamentals of Enhanced Oil Recovery. SPE.
• Pyrcz, M.J., Deutsch, C.V. (2014). Geostatistical Reservoir Modeling, 2nd ed. (Chapter on flow simulation integration.)
• Aziz, K., Settari, A. (1979). Petroleum Reservoir Simulation. Applied Science.