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

S. Roy, Kalyan Saikia
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引用次数: 0

Abstract

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.
基于三维地震属性和井资料标定的三维三维岩相识别与建模
地震属性通过提供对地下的间接了解,在储层表征和三维(3D)岩相建模中起着重要作用。在此类研究中使用地震属性一直具有挑战性,因为很难确定硬数据点(即井信息)与地震属性的三维体之间的现实关系。然而,基于概率的井数据三维地震属性校准方法可以提供更好的岩相建模和储层物性空间分布结果。本文提出了一种基于概率的地震属性标定技术,用于三维岩相建模和分布。该方法有助于地下储层的表征,并提供了真实的岩相分布模型。与简单使用地质统计算法的常规方法相比,该方法还有助于减少岩相预测的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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