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Enhanced delineation of reservoir compartmentalization from advanced preand post-stack seismic attribute analysis

机译:从高级叠前和叠后地震属性分析中更好地划分储层划分

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Reservoir compartmentalization has a huge bearing on fluid flow within hydrocarbon reservoirs, and can impact overall recovery during field development. Small and sub-seismic faults can have a dramatic effect on the compartmentalization within a reservoir, but until recently they have not typically been incorporated into fault interpretations. This can be due to data fidelity and the amount of time needed to manually pick them. Their omission from the interpretation - and ultimately reservoir models - means the understanding of reservoir compartmentalization is incomplete, hence solving this problem is critical to improve production. Approaches that automatically identify and extract faults from seismic volumes are available. These automated methods aim to emphasize discontinuities within seismic volumes and are usually focused on poststack data. However, they need preconditioned inputs that are often based around a coherence algorithm. This preconditioning aims to suppress noise but can inflict data degradation, which may diminish smaller features in the seismic volumes. This article proposes an enhanced approach using a new combination of preconditioning steps designed to avoid these degradation problems. It also proposes the use of prestack seismic data, which has not traditionally been used for this purpose. Analysis of various pre-stack elements is displayed to show it can delineate more features than poststack data alone in certain noisy areas, such as gas effects or low frequencies. Finally, it demonstrates that the best approach combines results from pre- and poststack analysis to produce a more complete picture of reservoir compartmentalization.
机译:储层的分隔对油气储层中的流体流动有很大的影响,并可能影响油田开发期间的整体采收率。小型和亚地震断层可能会对储层内的隔震作用产生重大影响,但直到最近,它们通常尚未被纳入断层解释中。这可能是由于数据保真度以及手动选择它们所需的时间量所致。解释的遗漏-以及最终的储层模型-意味着对储层划分的理解还不完整,因此解决此问题对于提高产量至关重要。可以使用自动识别地震体积并从中提取断层的方法。这些自动化方法旨在强调地震区内的不连续性,通常集中于叠后数据。但是,他们需要通常基于相干算法的预处理输入。这种预处理的目的是抑制噪声,但会造成数据降级,从而可能会减小地震波中较小的特征。本文提出了一种使用预处理步骤的新组合的增强方法,旨在避免这些降级问题。它还提出了使用叠前地震数据的方法,而这在传统上还没有用于此目的。显示了对各种叠前元素的分析,显示出在某些嘈杂区域(例如气体效应或低频),与单独叠后数据相比,它可以描绘出更多特征。最后,它证明了最好的方法结合了叠前和叠后分析的结果,以产生更完整的储层分隔图。

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