The frequently stated problem of under‐delivery in oil and gas exploration is largely due to overprediction in the volumetric size of prospects rather than to the misinterpretation of risk. In an effort to deal with the significant degree of uncertainty inherent in sub‐surface evaluations, the standard method involves building a stochastic volumetric model of the potential container by choosing distributions and probabilities of the gross rock volume, the simulated column height, and the average 3D net/gross, as well as of other reservoir and fluid parameters. Unfortunately, prior to drilling, the three main inputs to the model are difficult to constrain as they are closely tied to the seismic interpretation rather than to historical information. By contrast, a source of hard data is available from existing discoveries and wells in the form of statistics for the play or analogue play, the most useful of which are: (i) the footprint area of the discoveries; (ii) the properties of net reservoir, encapsulated in an area yield parameter MMboe/km²; and (iii) the downside size of the discoveries, specifically the inferred P99 recoverable resource. In this paper, we propose a method called Prospect Area Yield (PAY) to assess the potential size of an exploration prospect which simply integrates these statistical data with the most reliable information from seismic mapping. The main step involves calculating an upside volume by multiplying a mid‐case MMboe/km² yield with a mapped reasonable closure area for the prospect. This upside volume is assigned a probability which is currently assumed to be P10, implying that 90% of discovery outcomes will be smaller. A probabilistic distribution of the recoverable resource for the prospect is then produced by using the upside volume (P10) and the inferred P99 to construct a lognormal trend. The method can be tested by companies using lookbacks to fine‐tune the probability of the upside volume to ensure that exploration predictions effectively match historical reality. In the meantime, it is recommended that the PAY method, which is available as a free online tool, is used as a check on the results of stochastic models.

中文翻译：

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潜在面积收益率（PAY）方法：常规石油勘探中过分乐观的体积估计的补救方法

石油和天然气勘探中经常出现的供不应求的问题，很大程度上是由于对前景量的预测过高，而不是由于对风险的误解。为了处理地下评估固有的很大程度的不确定性，标准方法包括通过选择总岩石体积，模拟柱高和平均值的分布和概率来建立潜在容器的随机体积模型。 3D净/毛重以及其他储层和流体参数。不幸的是，在钻探之前，很难对模型的三个主要输入进行约束，因为它们与地震解释而不是历史信息紧密相关。相比之下，可以从现有的发现物和井中以统计形式获得有关该活动或类似活动的硬数据来源，其中最有用的是：（i）发现的足迹区域；（ii）以面积屈服参数MMboe / km封装的净储层的性质² ; （iii）发现的不利范围，特别是推断出的P99可采资源。在本文中，我们提出了一种称为“前景区产量”（PAY）的方法来评估勘探前景的潜在规模，该方法将这些统计数据与来自地震测绘的最可靠信息简单地集成在一起。主要步骤涉及通过乘以中等情况下的MMboe / km ²来计算上行空间并为潜在客户绘制了合理的封闭区域。当前空间的上升空间被分配为当前假定为P10的概率，这意味着90％的发现结果将较小。然后，通过使用上升量（P10）和推断的P99来构建对数正态趋势，来为潜在客户生成概率分布。公司可以使用回溯来测试该方法，以微调上行量的可能性，以确保勘探预测有效地符合历史现实。同时，建议使用PAY方法（可作为免费的在线工具使用）来检查随机模型的结果。