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Optimizing Estimates of Probabilistic Reserves from Production Trends Using aBayesian Approach

机译：使用贝叶斯方法从生产趋势中优化概率储量的估计

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The estimation of probabilistic reserves distributions isrnnowadays a mandatory task in the oil industry. The trade-offrnbetween speed and accuracy, coupled with the urgency ofrnresults is still making of Decline Curve Analysis (DCA) onernof the most popular method to address these calculations.rnIn a previous paper, we introduced a quick and efficientrnapplication (PREP) to estimate probabilistic reservesrndistribution combining the advantages of stochastic methodsrn(Bootstrapping) and DCA. Although the proposed tool isrnrobust and effective, one of the limitations found wasrnreconciling the statistical uncertainty associated in therngeneration of the DCA parameter distributions with thernobserved production trends.rnThe objective of this paper is to present a proficientrnprocedure to reduce the uncertainties in DCA probabilisticrnreserves estimation via Bayesian techniques. The procedurernallows the analysis of multiple decline trends taking fullrnadvantage of the self-learning capability implicit in Bayesianrntechniques. The usefulness of the results is maximized whenrncoupled with proper reservoir knowledge, leading tornstatistically strong results.rnThe new methodology was applied in the reservernevaluation of three (3) Colombian fields located in the VallernSuperior del Magdalena Basin in Colombia, South America.rnThe method provided a new set of declination parameters withrnsmaller covariance, reducing the uncertainty compared to anyrnprevious reserves distribution.
机译：如今，估计概率储量分布是石油行业的一项强制性任务。速度和精度之间的权衡取舍，再加上结果的紧迫性，仍然是最流行的方法来解决这些计算的下降曲线分析（DCA）问题。在以前的文章中，我们介绍了一种快速有效的应用程序（PREP）来估计概率储备分配结合了随机方法（Bootstrapping）和DCA的优点。尽管所提出的工具是鲁棒且有效的，但发现的局限之一是使DCA参数分布的生成中的统计不确定性与生产趋势趋向一致。技术。该程序允许充分利用贝叶斯技术中隐含的自学习能力来分析多个下降趋势。结合适当的储层知识，可以最大程度地发挥结果的作用，从而带来统计上强劲的结果。该新方法被用于对位于南美哥伦比亚的瓦莱恩上马格达莱纳盆地的三（3）个哥伦比亚油田进行储层评估。新的一组磁偏角参数具有较小的协方差，与以前的任何储量分布相比，都减少了不确定性。

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