The estimation of probabilistic reserves distributions is nowadays a mandatory task in the oil industry. The trade-off between speed and accuracy, coupled with the urgency of results is still making of Decline Curve Analysis (DCA) one of the most popular method to address these calculations.

In a previous paper, we introduced a quick and efficient application (PREP) to estimate probabilistic reserves distribution combining the advantages of stochastic methods (Bootstrapping) and DCA. Although the proposed tool is robust and effective, one of the limitations found was reconciling the statistical uncertainty associated in the generation of the DCA parameter distributions with the observed production trends.

The objective of this paper is to present a proficient procedure to reduce the uncertainties in DCA probabilistic reserves estimation via Bayesian techniques. The procedure allows the analysis of multiple decline trends taking full advantage of the self-learning capability implicit in Bayesian techniques. The usefulness of the results is maximized when coupled with proper reservoir knowledge, leading to statistically strong results.

The new methodology was applied in the reserve evaluation of three (3) Colombian fields located in the Valle Superior del Magdalena Basin in Colombia, South America. The method provided a new set of declination parameters with smaller covariance, reducing the uncertainty compared to any previous reserves distribution.

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