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Constraining sub-seismic deep-water stratal elements with electrofacies analysis; A case study from the Upper Cretaceous of the Måløy Slope, offshore Norway

机译:利用电相分析约束次地震深水地层元素;挪威近海måløy坡上白垩​​纪的案例研究

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摘要

Electrofacies represent rock facies identified from wireline-log measurements, and allow extrapolation of petrophysical characteristics away from stratigraphic intervals that are calibrated to core. This approach has been employed to reduce uncertainty in the identification of the sub-seismic depositional elements in the late Cenomanian-Coniacian succession of the northern Måløy Slope, offshore Norway. Core logging permits identification of eleven distinct sedimentary facies that are grouped into four facies associations: FA A-turbidite sandstones, FA B-heterolithic siltstones and sandstones, FA C-debrites and FA D-slide and slump deposits. Each facies association is defined by a distinct combination of petrophysical characteristics, including porosity, density, gamma-ray, sonic and resistivity. Using neural network analysis, electrofacies are calibrated with sedimentary facies, thereby allowing us to map their thickness and stacking patterns within the studied deep-water succession. We demonstrate that this approach is particularly useful where the presence of glauconite makes the distinction between sandstone- from shale-rich units difficult using gamma-ray logs alone. Our results indicate that the succession of interest is dominated by debris flows and slide and slump deposits, which are commonly poorly imaged on seismic reflection datasets in the northern North Sea. The methodology presented here can aid the correlation of deep-water stratal elements at production and exploration scales in stratigraphic successions that have undergone similar burial histories.Furthermore, this method may help in the identification of mass flow deposits that are present in Upper Cretaceous deep-water systems of the North Sea.
机译:电相表示从测井测井数据中识别出的岩石相,并允许将岩石物理特征外推到校准到岩心的地层间隔之外。在挪威海上北部Måløy斜坡北缘的晚西诺曼尼亚-科尼亚古相继发中,已采用这种方法来减少亚地震沉积元素识别的不确定性。岩心测井可以识别11个不同的沉积相,这些沉积相可分为4个相联系:FA A浊石砂岩,FA B异质粉砂岩和砂岩,FA C碎屑岩和FA D滑坡和塌落沉积物。每个相的联系都由岩石物理特征(包括孔隙度,密度,伽马射线,声波和电阻率)的不同组合来定义。使用神经网络分析,用沉积相校准电相,从而使我们能够在研究的深水演替过程中绘制其厚度和堆积模式。我们证明,在仅使用伽马射线测井仪就难以区分砂岩和富页岩的地区的情况下,这种方法特别有用。我们的结果表明,感兴趣的演替以泥石流,滑坡和坍落度沉积物为主,这些沉积物通常在北海北部的地震反射数据集上成像不佳。这里介绍的方法可以帮助经历了相似埋葬历史的地层演替中深水地层元素在生产和勘探规模上的相关性。北海的水系统。

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