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Detection of fluvial sand systems using seismic attributes and continuous wavelet transform spectral decomposition: case study from the Gulf of Thailand

机译:利用地震属性和连续小波变换谱分解检测河流砂土系统:以泰国湾为例

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摘要

Fluvial sands host excellent oil and gas reservoirs in various fields throughout the world. However, the lateral heterogeneity of reservoir properties within these reservoirs can be significant and determining the distribution of good reservoirs is a challenge. This study attempts to predict sand distribution within fluvial depositional systems by applying the Continuous Wavelet Transformation technique of spectral decomposition along with full spectrum seismic attributes, to a 3D seismic data set in the Pattani Basin, Gulf of Thailand. Full spectrum seismic attributes such as root mean square and coherency help to effectively map fluvial systems down to certain depth below which imaging is difficult in the intervals of interest in this study. However, continuous wavelet transform used in conjunction with other attributes by applying visualization techniques of transparency and RGB can be used at greater depths to extract from 3D seismic data useful information of fluvial depositional elements. This workflow may help to identify different reservoir compartments within the fluvial systems of the Gulf of Thailand.
机译:河流相砂在全球各个领域拥有优良的油气藏。然而,这些储层内储层性质的横向非均质性可能很显着,确定好储层的分布是一个挑战。这项研究试图通过将光谱分解的连续小波变换技术与全光谱地震属性一起应用到泰国湾北大年盆地的3D地震数据集中,来预测河流沉积系统内的沙子分布。诸如均方根和相干性之类的全谱地震属性有助于有效地将河流系统映射到一定深度,在该深度以下,在本研究的关注区间很难成像。但是,通过应用透明度和RGB可视化技术,与其他属性结合使用的连续小波变换可以在更大的深度使用,以从3D地震数据中提取河流沉积元素的有用信息。该工作流程可能有助于识别泰国湾河流系统内的不同储层隔室。

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