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Three-Dimensional 3D Lithofacies Identification and Modeling Using 3D Seismic Attribute and Well Data Calibration

机译:三维3D锂外识别和使用3D地震属性和井数据校准的建模

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Seismic attributes play an important role during reservoir characterization and three-dimensional (3D) lithofacies modeling by providing indirect insight of the subsurface. Using seismic attributes for such studies has always been challenging because it is difficult to determine a realistic relationship between hard data points (i.e., well information) and a 3D volume of seismic attributes. However, a probability-based approach for 3D seismic attribute calibration with well data provides better results of lithofacies modeling and spatial distribution of reservoir properties. This paper presents a probability-based seismic attribute calibration technique that has been described for 3D lithofacies modeling and distribution. This approach helps in subsurface reservoir characterization and provides a realistic lithofacies distribution model. This approach also helps reduce uncertainty of lithofacies prediction compared to conventional methods of simply using geostatistical algorithms.
机译:地震属性在储层特征和三维(3D)锂外思想中通过提供了对地下的间接洞察力来发挥重要作用。 使用地震属性进行这种研究一直在具有挑战性,因为很难确定硬数据点(即,井信息)与地震属性的3D体积之间的现实关系。 然而,具有井数据的3D地震属性校准的基于概率的方法提供了Lithofacies建模和储层性能的空间分布的更好结果。 本文介绍了一种基于概率的地震属性校准技术,已被描述为3D锂外造型建模和分布。 这种方法有助于地下储层特征,并提供现实的锂外分布模型。 与简单地使用地统计算法的传统方法相比,这种方法还有助于减少岩散缩损预测的不确定性。

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