Oil and Gas

UDOMORE stochastic workflow for reservoir structure modeling

The UDOMORE stochastic workflow for reservoir structure achieves consistent uncertainty quantification and propagation in structure modeling and volumetrics computations.

Approach

At Seisquare, we place uncertainty characterization at the heart of structure modeling and volumetrics computations, and achieve depth and volume predictions with maximum accuracy and confidence. Our approach follows a three-step process.

Step 1: Perform spatial conditioning of the data sets considered for depth conversion using advanced factorial kriging techniques. This step produces data which is suitable for depth conversion and systematically associated with quantified uncertainty values.

Step 2: Perform stochastic depth conversion using advanced Bayesian Kriging algorithms. This results in reliable depth predictions associated with quantified uncertainty.

Step 3: Perform stochastic volumetrics computations using Bayesian simulation algorithms. From simulated depth maps, we derive reliable GRV expectation curves, reservoir probability maps and spill point localization maps.

Benefits

The UDOMORE stochastic workflow for reservoir structure has been applied in numerous, varied operational contexts with compelling results. The workflow produces reliable depth and volumetrics predictions with a quantified probablilty of success. These results can be used to provide confidence when making critical E&P decisions such as prospecting, locating a new well in a reservoir, or defining a production strategy.

Operational applications

Associated products

 




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