Uncertainty Quantification

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More Info: Brochure, SPE Paper 181374

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The Evolve^® Workflow

The goal of the first stage is to extract a smaller ensemble of geomodels that is representative of the diversity of a much larger ensemble. In this screeing step, the concern is not how models compare to measured production data but rather how model responses compare to each other. Model diversity is identified through multidimensional scaling (MDS)  along with cluster analysis. An efficient flow modeling proxy to compare model responses is essential at this stage as the goal is to run as wide and diverse ensemble of static geological models as possible (100s to 1000s of models).

Screening multiple geomodels through 3DSL, followed by clustering, and then extracting a much smaller representative subset of models.

Stage 2: Field Level Calibration

The geo-ensemble extracted from Stage 1 is combined with global flow simulation uncertainties (such as OWC depth, relperm functions, PVT properties,...) increasing again the ensemble size.  The goal of this stage is to reduce the size of the ensemble while minimizing the error to historical data and maximizing input parameter diversity. This an optimization problem.

Include uncertainty in flow simulation parameters such as PVT properties, contact depths, and relperms.  Then optimize on these flow parameters and extract a set of models that are close to history while retaining input parameter diversity.

Stage 3: Well-Level Calibration

Stage 2 will yield an ensemble of models that display an acceptable match of the field response. Individual well responses, however, are not guaranteed to exhibit the same match. At this stage, a novel well-level history matching algorithm is used to modify inter-well geology to improve well matches for all models of the ensemble, or a selected subset.

Automatically select the worst well in each model and then perform well-level history matching on these wells, such that each model in the ensemble has both a good field-level and well-level match.

Stage 4: Forecasting & Economics

The final step of the EVOLVE workflow is to use the ensemble of models extracted from stage 2 or stage 3 for forecasting and ecomomic analysis. The ensemble of models is considered robust and practical: robust because the models exhibit diversity, practical because the number of models remains manageable for computing purposes. The ensemble can now be used to investigate short and long term optimization strategies under various economic scenarios.

For each history matched model in the final ensemble, attach multiple forecast scenarios (base case, infill drilling, polymer injection, etc).  Then for each forecast scenario include uncertainties in NPV by including multiple economic scenarios.