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Uncertainty in Geostatistical Modeling

Uncertainty in Geostatistical Modeling. Where uncertainties come from. Various sources seismic, well-logs, core,well -testing Various Scales But on the other hand, geostatistics contributes to the data integration Geostastics is an interdisplinary tool.

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Uncertainty in Geostatistical Modeling

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  1. Uncertainty in Geostatistical Modeling

  2. Where uncertainties come from • Various sources seismic, well-logs, core,well-testing • Various Scales • But on the other hand, geostatistics contributes to the data integration • Geostastics is an interdisplinary tool

  3. Can uncertainties be estimated? • No! • “Rather it is modeled. All uncertainties measures are models, all based on somewhat subjective decisions about what should be considered uncertain or soft and what should be considered unquestionable or hard.”—Journel • But it’s still better to create a subjective model of uncertainty than an illusion of certainty

  4. What subjective decisions we made? • Which data is hard data? Which data is soft data? • Variogram model • Which simulation(modeling) algorithm to use?

  5. Thus..Uncertainties • Uncertainties in hard and soft data • Uncertainties in variogram model • Uncertainties in the input parameters

  6. Enhancements • Uncertainties in hard and soft data Quality Check secondary data(geological maps and seismically derived attributes)-multiple representations • Uncertainties in variogram model Integrating analog geologic data(Kupfersberger 1999, Jerry 2001) End-members for distribution of ranges(Ortiz 2002) • Uncertainties in input parameters hierarchical modeling multiple realizations advanced modeling schemes sensitivity analysis(uncertainty assessment)

  7. Hierarchical modeling • Facies modeling  Petrophysical modeling Andres,AAPG book,1994 • Rock type  Reservoir property modeling Christopher J. Murray, AAPG book,1994

  8. Advanced modeling schemes • Combining deterministic and stochastic methods --SESIMIRA(object-based) Andres,AAPG book,1994 • Combining SIS and annealing method Christopher J. Murray, AAPG book,1994 • Gaussian collocated cosimulation algorithm Alberto, AAPG book, 1994

  9. Multiple realizations • “For the purpose of generating realizations of the full reservoir model that express the uncertainty in the input statistics, it is recommended that multiple realizations of each property be produced” • Any realization of one reservoir property may be dependent on the preceding realization of another

  10. New methods for assessing uncertainties in groundwater models • Selecting and analyzing a simpler surrogate model that has many of the characteristics of the process model to be evaluated, due to the significant amount of time assessing the uncertainty in large models Keating et al., WRR, 2010

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