None of the currently proposed correlations for bubblepoint pressure are particularly accurate.

Knowledge of bubblepoint pressure is one of the important factors in the primary and subsequent developments of an oil field. Bubblepoint pressure is required for material balance calculations, analysis of well performance, reservoir simulation, and production engineering calculations.

In addition, bubblepoint pressure is an ingredient, either directly or indirectly, in every oil property correlation. Thus an error in bubblepoint pressure will cause errors in estimates of all oil properties. These will propagate additional errors throughout all reservoir and production engineering calculations.

Bubblepoint pressure correlations use data which are typically available in the field; initial producing gas-oil ratio, separator gas specific gravity, stock-tank oil gravity, and reservoir temperature. The lack of accuracy of current bubblepoint pressure correlations seems to be due to an inadequate description of the process-in short, one or more relevant variables are missing in these correlations.

We considered three independent means for developing bubblepoint pressure correlations. These are (1) non-linear regression of a model (traditional approach), (2) neural network models, and (3) non-parametric regression (a statistical approach which constructs the functional relationship between dependent and independent variables, without bias towards a particular model).

The results, using a variety of techniques (and models), establish a clear bound on the accuracy of bubblepoint pressure correlations. Thus, we have a validation of error bounds on bubblepoint pressure correlations.

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