Repeatable results

Repeatable results

Legacy reservoir modeling and simulation workflows aim at building a single base case reservoir model. Since at the outset there are many unknowns and uncertainties, the process to converge onto the base case involves making educated guesses for the unknown items, and reducing the uncertainties to a handful that can be identified as risk factors and then mitigated in the final iterations. Over the many years of an asset’s life cycle from exploration to final production, such workflows will be applied by different teams, and each time some component of bias will cause the results to differ.

Resoptima solution

By design the Resoptima workflow comprising ResX and IRMA is identical at all stages of the asset’s life cycle, and all available data is always used. Ensemble-based modeling only requires some initial choices regarding the geological hypothesis on which the modeling will be run, since all possible solutions are retained. Because subject matter experts are not required to make individual decisions or to eliminate uncertainties during the computation phase, the result is both repeatable and unbiased for a given initial hypothesis. The only variations that occur over time are due to new data being available.

The workflow is the same at all stages of the asset’s life cycle

The benefits of ensemble modeling

There are two main implications stemming from this approach:

  • The modeling and simulation elements provided in support of a business proposition are known to include all the actual uncertainties and available facts related to the asset
  • The results being presented could have been produced by any competent team, and will be replicated if the process is run again by the same or a different team

So, while the actual decisions that are taken for a given business case will vary depending on the operator’s available resources, portfolio priorities and appetite for risk, the information that underpins that decision is unique and it is the best possible at that point in time.

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