How Incorporating More Data Reduces Uncertainty in Recovery Predictions
Fernando P. Campozana, Larry W. Lake, Kamy Sepehrnoori, 1999. "How Incorporating More Data Reduces Uncertainty in Recovery Predictions", Reservoir Characterization—Recent Advances, Richard A. Schatzinger, John F. Jordan
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From the discovery to the abandonment of a petroleum reservoir, there are many decisions that involve economic risks because of uncertainty in the production forecast. This uncertainty may be quantified by performing stochastic reservoir modeling (SRM); however, it is not practical to apply SRM to account for new data every time the model is updated.
This paper suggests a novel procedure to estimate reservoir uncertainty (and its reduction) as a function of the amount and type of data used in the reservoir modeling. Two types of data are analyzed: conditioning data and well-test data; however, the same procedure can be applied to other data types.
SRM is typically performed for the following stages: discovery, primary production, secondary production, and infill drilling. From those results, a set of curves is generated that can be used to estimate (1) the uncertainty for any other situation and (2) the uncertainty reduction caused by the introduction of new wells (with and without well-test data) into the description.