Master geostatistical workflow card

This is the closing reference card for the Geostatistics textbook. Walk through every reservoir-characterisation project — clastic, carbonate, fluvial, tight-gas, heavy-oil EOR, CO₂ storage — by applying the decision graph below. Each row is a workflow stage; the right column is the textbook section.

Stage 1 — Frame the decision question

• What is the DECISION RIDING ON THIS WORK? OOIP for development go/no-go? Well-spacing for drainage planning? CO₂ storage approval?
• What is the QUANTITY OF INTEREST? Mean (OOIP), connectivity (drainage), extreme (P-failure), uncertainty range (P90/P10)?
• What are the THRESHOLDS that distinguish acceptable from unacceptable outcomes? Document these BEFORE the analysis.

Stage 2 — Assemble and prepare the data

• Well-log data: porosity, permeability, facies indicator, water saturation.
• Seismic-derived attributes: impedance, AVO products, time-lapse signatures (§4.5 cosimulation context).
• Declustering (§§2.1-2.3): for irregular well spacing, compute declustering weights before univariate statistics.
• Normal-score transform (§1.4): on the declustered data, NOT the raw data.

Stage 3 — Build the spatial-correlation model

• Experimental variogram (§3): compute on the declustered, NS-transformed data; multiple directions for anisotropy detection.
• Variogram model fit (§4): spherical / exponential / Gaussian; permit nested structures; flag the nugget effect.
• Sensitivity: rerun downstream simulations with alternative variogram parameters to bound the variogram-model uncertainty.

Stage 4 — Choose the estimation method

• Continuous Gaussian property + linear interpolation goal: ORDINARY KRIGING (§5.2).
• Need uncertainty per cell + many realizations: SEQUENTIAL GAUSSIAN SIMULATION (§§7.1-7.4).
• Categorical / facies modeling: indicator simulation (§§8.2-8.5).
• Channels, complex geometry, multipoint patterns: MPS / SNESIM (§§9.2-9.3).
• Modern generative ML (§9.4): only when validated against MPS and conditioning-data fidelity proven.
• Method choice driven by THREE axes (§9.5): variable type, spatial structure, data context.

Stage 5 — Run, validate, and quantify uncertainty

• Number of realizations: 20+ for OOIP-type integrated metrics; 100+ for extreme-event probabilities (CO₂ P-failure, fracture-network connectivity).
• Validate each realization against the variogram, against well data exact-honoring, and against geological plausibility.
• Cross-validate via leave-one-out (§6.1-6.3): kriging-variance vs observed mean-squared error.
• Compute the decision-relevant quantity across realizations: P10/P50/P90 of OOIP, drainage area, P-failure.

Stage 6 — Integrate auxiliary data

• Cosimulation with seismic attributes (§5.3 KED, §6 cokriging): dense secondary data constrains realizations between wells.
• For 4D / EOR (§10.5): time-lapse seismic informs sweep history; use Bayesian/ensemble methods to update realizations against observed 4D signatures.
• Geomechanical / fracture data (§10.4): integrate as secondary input to facies indicator or as separate fracture-density realization.

Stage 7 — Communicate

• Report P10 / P50 / P90 of the decision-relevant quantity.
• State the VARIOGRAM MODEL explicitly: range, sill, nugget, anisotropy. Show alternative models that bracket the range.
• State the CONDITIONING-DATA fidelity: did realizations exactly honor wells? If MPS, was conditioning successful or approximate?
• For high-stakes claims (CO₂ approval, billion-dollar development): show the sensitivity of conclusions to variogram parameters AND to the choice of estimation method.
• NEVER report a single deterministic kriging map as if it captures uncertainty — kriging is the conditional mean only.

The two big traps to avoid

• Single-realization decisions: presenting kriging as "the answer". Kriging is the MEAN; the SPREAD across SGS realizations is the uncertainty. Report both, always.
• Wrong-method choice (§9.5): SGS on a fluvial reservoir misses channel connectivity; MPS on a homogeneous-shelf reservoir overspecifies. Match the method to the geology.

Closing thought

Reservoir characterisation is a CRAFT. The math (kriging equations, variogram models, MPS templates) is necessary but not sufficient. You must integrate it with geology, geophysics, reservoir engineering, and a clear understanding of what decisions the work will inform. The best geostatistical workflow is the one that gives the development team an honest distribution of outcomes — including the uncertainty they don't want to see.

You have completed the Geostatistics curriculum. Apply this material to real projects; teach it to colleagues; revisit it when new data acquisition technologies emerge (4D seismic, fibre optics, microseismic monitoring, AI-derived facies probabilities). The field will evolve, but the foundational ideas — variograms, kriging, SGS, MPS, uncertainty as ensemble — will remain.

Mark workflow card complete →

References — the close-of-textbook reading list

• Pyrcz, M.J., Deutsch, C.V. (2014). Geostatistical Reservoir Modeling, 2nd ed. Oxford. (The practitioner's comprehensive reference.)
• Deutsch, C.V., Journel, A.G. (1998). GSLIB: Geostatistical Software Library, 2nd ed. Oxford. (The canonical algorithms reference.)
• Chilès, J.-P., Delfiner, P. (2012). Geostatistics: Modeling Spatial Uncertainty, 2nd ed. Wiley. (The theoretical-rigorous reference.)
• Caers, J. (2011). Modeling Uncertainty in the Earth Sciences. Wiley. (Methodological framework, beautifully written.)
• Mariethoz, G., Caers, J. (2014). Multiple-point Geostatistics. Wiley-Blackwell. (MPS methods.)
• Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford. (Classical reference, still indispensable.)
• Cressie, N. (1993). Statistics for Spatial Data. Wiley. (The statistical-theoretical reference for spatial statistics.)
• Wackernagel, H. (2003). Multivariate Geostatistics, 3rd ed. Springer. (Cokriging and multivariate methods.)
• Isaaks, E.H., Srivastava, R.M. (1989). An Introduction to Applied Geostatistics. Oxford. (Excellent introductory reference.)
• Rossi, M.E., Deutsch, C.V. (2014). Mineral Resource Estimation. Springer. (Mining-specific geostatistics applications.)