Capstone 2: Carbonate reservoir with facies modeling

Carbonate reservoirs differ from clastic shelves: porosity is BIMODAL — high in grainstones and packstones, low in mud-dominated facies. A two-step workflow (facies first, porosity within facies) captures this geology far better than a single Gaussian SGS on porosity alone.

Why facies-first matters

If you ignore facies and run pure SGS on porosity in a carbonate, the model has the right average φ but the WRONG variance structure: realizations look smoothly varying instead of sharply transitioning. OOIP estimates miss the connectivity and compartmentalisation that drive flow performance. The two-step approach captures the geometry first, then the within-facies property variation.

Workflow stages

• Facies-indicator variogram: fit the variogram on the BINARY grainstone indicator. Range typically shorter than for porosity (facies switch faster than continuous attributes).
• SISIM / indicator simulation: produce 20+ FACIES realizations honoring the target grainstone fraction (e.g., 40%).
• Per-facies porosity SGS: in each realization, fill grainstone cells with φ ~ N(0.22, 0.05²); fill mudstone cells with φ ~ N(0.06, 0.02²). Separate range parameters per facies are common.
• OOIP rollup: per realization, compute Σ cells φ(c) × thickness × area × (1 - Sw) / Bo. Histogram across realizations gives P10/P50/P90.

Uncertainty in carbonate OOIP comes from TWO sources combined:

• FACIES UNCERTAINTY — how much grainstone vs mudstone, and where
• WITHIN-FACIES POROSITY UNCERTAINTY — variation around the facies-conditional mean

Try it

• Defaults (40% grainstone, range = 8, μ_g = 0.22, μ_m = 0.06). Note the bimodal porosity in realization 1 — clearly distinct grainstone (high) and mudstone (low) regions. OOIP P90/P10 ratio reflects the combined facies + within-facies uncertainty.
• Increase grainstone fraction to 0.6. OOIP shifts upward (more high-φ rock). Notice the facies map becomes dominated by grainstone.
• Set mean φ_grainstone = 0.30 (very high) and μ_mud = 0.02 (very low). The bimodal contrast becomes extreme; OOIP becomes very sensitive to the grainstone fraction.
• Reduce facies range to 3 (small-scale heterogeneity). The facies maps become much patchier; realizations look very different from each other. OOIP P90/P10 ratio rises.
• Compare against a hypothetical pure-φ SGS with mean = 0.4·0.22 + 0.6·0.06 = 0.15 and sd around 0.08. The mean is the same, but the realizations would look smooth — losing the geological meaning entirely.

An asset team has 25 wells, 10 of which are in grainstone-dominated facies. The first 20% of the reservoir thickness is logged as grain-dominated, then transitions to mud-dominated. How should you encode this VERTICAL TREND in the facies modeling workflow, and what is the consequence of ignoring it?

What you now know

You can run a two-step carbonate workflow: facies first, then per-facies porosity. The same approach scales to multi-facies (3+ rock types) and to 3D grids. The widget's indicator-then-Gaussian approach is the basis of SISIM in real geostatistical software — the GSLIB SISIM program implements this exactly, with proper variogram-driven sequential indicator simulation.

Mark capstone complete →

References

• Pyrcz, M.J., Deutsch, C.V. (2014). Geostatistical Reservoir Modeling, 2nd ed. Oxford. (Carbonate facies modeling chapter.)
• Lucia, F.J. (2007). Carbonate Reservoir Characterization: An Integrated Approach, 2nd ed. Springer.
• Deutsch, C.V., Journel, A.G. (1998). GSLIB. SISIM module reference.
• Strebelle, S. (2002). "Conditional simulation of complex geological structures using multiple-point statistics." Math. Geol. 34(1).
• Pyrcz, M.J., Boisvert, J.B., Deutsch, C.V. (2008). "Multipoint and object-based modeling of meandering river reservoirs." Petroleum Geoscience 14, 119-135.