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Rethinking Appraisal: Identification of Pre- and Post-Sanction Uncertainty Drivers in Deep and Ultra Deep Gulf of Mexico Fields Using Data Mining and Data Analytics

机译：重新思考评估：使用数据挖掘和数据分析识别墨西哥湾深部和超深部油田制裁前后的不确定因素

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Appraisal is a key step in consenting to develop an asset, or abandoning it, and is pursued after successful drilling of an exploration well in a potential field. During the appraisal process the drainage area and original hydrocarbons in place, as well as ultimate recovery (EUR) from the field are estimated which are often based on minimum set of information gathered during the exploration phase. This lack of data, along with uncertainties surrounding the appraisal data, introduces high degrees of variations in pre- and post- sanction EURs (EUR). These estimates, however, are revisited each time new data becomes available and as a result, the EUR from a field, along with several other factors, is subject to change over the field lifespan. Identifying the key drivers in accurate pre-sanction estimation of ultimate recovery and reducing post sanction EUR variance, helps in resource allocation and sustainable field development. A major hurdle faced in subsurface characterization of assets is the degree of dependency between attributes and, the often non-linear behavior of these attributes. One way of overcoming these limitations is regression analysis; however, even in a high accuracy fit, regression coefficients by themselves are not necessarily good measures for ranking attributes, and elimination of lower ranked attributes would result in a new ranking of the remaining attributes. In the present study, several data mining techniques are applied on a dataset of 152 deep and ultra-deep water (D&UDW) fields in the Gulf of Mexico (GoM) to determine which of the 77 well-, reservoir- and field-scale attributes best capture the EUR variance for different fluid types in D&UDW fields in the GoM. Unlike the conventional regression approaches, the present study offers a robust and stable ranking of attributes with high accuracy fit, where low to none contributing (poorly-predictive) attributes can be safely removed without changing the overall ranking of higher attributes. This ensures that a high ranked attribute is indeed a major contributor to accurate estimation of the ultimate recovery from a field, and therefore is worth the investment for capturing its value; on the other hand, a low ranked attribute, in all likelihood, is a redundant attribute and should not be collected; this would in turn free up resources that can be allocated to acquisition of high(er) ranking attributes. Results of this study identify attributes that are strong overall drivers in over/under - estimation of reserves in pre- and post- sanction stages. We have also ranked the key attributes to reliable EUR estimations, which should be acquired prior to commitment to sanction. In addition, a set of attributes that have been consistently ranked as poor predictors are identified, which can be safely eliminated from data acquisition without affecting appraisal accuracy. Since the database tested was substantial covering all D&UDW fields in GoM, the identified key drivers have broad coverage and application.
机译：评估是同意开发或放弃资产的关键步骤，是在潜在领域成功钻探一口勘探井之后进行的。在评估过程中，通常根据勘探阶段收集到的最少信息来估算流域的排水面积和原位碳氢化合物以及油田的最终采收率（EUR）。数据的缺乏以及评估数据的不确定性，导致制裁前和制裁后欧元（EUR）的高度差异。但是，每当有新数据可用时，都会重新评估这些估算值，因此，油田的欧元以及其他一些因素在油田的使用寿命中可能会发生变化。确定关键的推动因素，以进行准确的最终制裁前估计，并减少制裁后的欧元差异，有助于资源分配和可持续的油田开发。资产的地下表征面临的主要障碍是属性之间的依赖程度，以及这些属性通常的非线性行为。克服这些局限性的一种方法是回归分析。但是，即使以高精度拟合，回归系数本身也不一定是对属性进行排名的良好度量，而消除排名较低的属性将导致对其余属性进行新的排名。在本研究中，将多种数据挖掘技术应用于墨西哥湾（GoM）的152个深水和超深水（D＆UDW）油田的数据集，以确定77口井，储层和油田规模属性中的哪一个最好在GoM中的D＆UDW字段中捕获不同流体类型的EUR方差。与传统的回归方法不同，本研究提供了具有高精确度拟合的稳健而稳定的属性等级，其中可以安全地删除低至无贡献（预测性差）的属性，而无需更改较高属性的总体等级。这确保了高等级的属性的确是准确估算田地最终采收率的主要因素，因此值得为获得其价值而进行投资;另一方面，排名较低的属性很可能是多余的属性，不应收集;反过来，这将释放可分配给较高（较高）排名属性的资源。这项研究的结果确定了在制裁前后评估储量的过高/不足的整体因素。我们还对可靠的欧元估算值的主要属性进行了排名，这些值应在承诺实施制裁之前获取。此外，还可以识别出一组始终被认为是不良预测指标的属性，这些属性可以安全地从数据采集中消除，而不会影响评估准确性。由于测试的数据库相当庞大，涵盖了GoM中的所有D＆UDW字段，因此确定的关键驱动因素具有广泛的覆盖范围和应用范围。

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