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Multivariate Study of the Utica: Marrying the Rock to the Completion

机译:尤尼卡的多变量研究:将岩石嫁给完成

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One of the most substantial challenges currently faced in many unconventional plays is linking subsurface fundamentals and empirical results/measured data, e.g., type curves, to predictive production. One way to achieve this link between subsurface fundamentals and well production for optimal completion techniques is through multivariate analysis. Roth (2013) showed this method has been used for sweet spot mapping, but in field development of unconventionals today, the next best well that meets the economics will be drilled and completed regardless of whether it is in the sweet spot or not. The more powerful use of this approach, therefore, is marrying the rock to the completion to optimize well performance. In unconventional reservoirs, production is driven by the complex interplay of engineering and geology. There are no bi-variate crossplots of engineering, geological, or geophysical variables that are capable of adequately describing the deliverability of the reservoir. Numerous engineering and geologic attributes must be considered simultaneously using multivariate techniques, such as multiple regression analysis, in order to properly model production. To understand key play driver relationships, well performance must be normalized with engineering data in order to isolate the impact of geologic attributes on well performance. In this study, a well performance predictive model was created in the Utica from identified key play drivers that combined engineering, geologic, and geophysical parameters to test different completion designs across areas of varying geology. Using this predictive model, which was supported by independent data from current analytical modeling and competitor data, the completion design was changed for a well pad drilled and completed in early 2017. The model predicted the of this pad to be approximately 18, which was the upper limits of performance input into the model. Initial production and tubing pressure show this pad exceeded our expectations with an of 23, which is the highest in the field.
机译:目前在许多非传统竞争中面临的最大挑战之一是将地下基础知识和经验结果/测量数据联系起来,例如曲线,预测生产。实现地下基础和良好生产之间实现这一联系的一种方法是通过多变量分析。 Roth(2013)显示这种方法已被用于甜蜜点映射,但在今天的非传统的现场开发中,符合经济学的下一个良好井将被钻探并完成,无论是在甜点还是没有。因此,这种方法越强大地使用岩石将岩石结婚,以优化良好的性能。在非传统水库中,生产是由工程和地质的复杂相互作用驱动的。没有能够充分描述储层的可交换性的工程,地质或地球物理变量的双变化交叉点。必须使用多变量技术同时考虑许多工程和地质属性,例如多元回归分析,以便正确模拟生产。要了解关键播放驾驶员关系,必须使用工程数据归一化良好的性能,以隔离地质属性对性能的影响。在这项研究中,在UTIC中创建了一种良好的性能预测模型,从鉴定的关键播放驱动程序组合工程,地质和地球物理参数来测试不同地质区域的不同完成设计。使用此预测模型,由来自当前分析建模和竞争对手数据的独立数据支持,完成设计已更改为2017年初钻取并完成的井垫更改。该模型预测了该垫的约18,即模型中性能的上限。初始生产和管道压力显示该垫上超过了我们的期望,其中23个是该领域中最高的。

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