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Identification of shallow geohazard channels based on discontinuity seismic attributes in the South Caspian Sea

机译:基于南方海运中的不连续地震属性的浅层地质锯齿识别

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Identification of geomorphological features in seismic data is a key element of seismic interpretation. Channels in the shallow subsurface are potential geohazards. At deeper levels, they can be the actual targets for (horizontal) drilling. Either way, it is important to optimally delineate these features prior to well location positioning and drilling. We have studied a poststack 3D seismic data from the South Caspian Sea featuring shallow channels that are considered potential geohazards for drilling operations. In the first step, we attenuate the acquisition footprints along the inline direction using a geostatistics approach based on factorial kriging. To better visualize channels in the presence of stratigraphic dips, we create a dense set of horizons using an inversion-based flattening algorithm. In the next step, we compare various discontinuity attributes such as semblance, similarity, curvature, and the relatively new attribute based on the multiscale and multidirectional shearlet transformation to determine which one best images our features of interest. Curvature attributes clearly image channel levies (positive curvature) and channel centers (negative curvature). Lateral changes in the curvature magnitude infer sedimentation from the north. Similarity, semblance, and shearlet transform attributes also successfully delineate channel edges, but these attributes do not contain additional geologic information. In the final step, we qualitatively analyze channel thickness variations by the red-green-blue blending of three spectral components based on short window Fourier transforms.
机译:地震数据中的地貌特征的识别是地震解释的关键因素。浅层地下的频道是潜在的地质锯齿。在更深层次的水平下,它们可以是(水平)钻井的实际目标。无论哪种方式,重要的是在井位置定位和钻孔之前最佳地描绘这些特征。我们研究了来自南部Caspian海的后攻击3D地震数据,具有浅渠道,被认为是用于钻井作业的潜在地质曲线。在第一步中,我们使用基于因子克里格的地统计方法方法沿着内联方向验证收购占地面积。为了更好地在地层倾斜的情况下可视化频道,我们使用基于反演的扁平化算法创建了一组密集的视野。在下一步中,我们基于多尺度和多向剪切变换比较各种不连续属性,例如外观,相似性,曲率和相对较新的属性,以确定哪一个最佳图像我们的感兴趣的特征。曲率属性明显地图像渠道征率(正曲率)和频道中心(负曲率)。北方曲率幅度推断沉降的横向变化。相似性,外观和Shearlet变换属性也成功地描绘了通道边缘,但这些属性不包含其他地质信息。在最后一步中,我们基于短窗傅里叶变换的三个光谱分量的红绿蓝色混合来定性分析通道厚度变化。

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