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首页> 外文期刊>Journal of Seismic Exploration >SIGNIFICANCE OF SUITABLE WAVELET ESTIMATION TO THE ANALYSIS OF SPECTRAL DECOMPOSITION METHOD TO DETECT CHANNEL FEATURE: A CASE STUDY IN THE JAISALMER SUB-BASIN, INDIA
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SIGNIFICANCE OF SUITABLE WAVELET ESTIMATION TO THE ANALYSIS OF SPECTRAL DECOMPOSITION METHOD TO DETECT CHANNEL FEATURE: A CASE STUDY IN THE JAISALMER SUB-BASIN, INDIA

机译：合适的小波估计对频谱分解方法分析检测信道特征的意义 - 以斋沙默盆地，印度案例研究

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摘要

Capturing the reservoir body in complex geology through regular attribute analysis is a challenging task. Subsurface imaging based on spectral decomposition analysis shows an improvement procedure for hydrocarbon exploration, especially in the complex geological setup. The spectral decomposition study was carried out in the Jaisalmer sub-basin. The sedimentary basin has the potential for hydrocarbon exploration. However, frequent alternation of lithology in the clastic and carbonate reservoir formation has made the exploration task challenging. Wavelet pattern recognition is a fundamental aspect of this process. The Continuous Wavelet Transformation (CWT) method was adopted to carry out the spectral decomposition study. A suitable wavelet was identified to characterize the reservoir lithology. The Gaussian wavelet produced a better and optimized outcome in this study than the other wavelets, such as Morlet and Mexican Hat. Few advanced attribute analyses such as geo-body capture and variance study were carried out based on volume rendering through the RGB blending process. The process was adopted using spectrally decomposed volume. The attribute analysis has produced an image that shows the extension of the reservoir lithology in the study area. One paleochannel was identified based on this study in the Pariwar formation as a potential reservoir architecture of hydrocarbon exploration.
机译：通过常规属性分析捕获复杂地质中复杂地质的水库体是一个具有挑战性的任务。基于光谱分解分析的地下成像显示了碳氢化合物勘探的改进程序，尤其是在复杂的地质设置中。光谱分解研究在Jaisalmer子盆地中进行。沉积盆地具有碳氢化合物勘探。然而，岩石和碳酸盐储层形成频繁交替使勘探任务具有具有挑战性。小波模式识别是此过程的基本方面。采用连续小波变换（CWT）方法进行光谱分解研究。鉴定了合适的小波以表征储层岩性。高斯小波在这项研究中产生了比其他小波更好，优化的结果，例如Morlet和墨西哥帽。利用RGB混合过程的体积渲染进行了很少的高级属性分析，例如地理捕获和方差研究。使用光谱分解的体积采用该方法。属性分析已经产生了一个图像，该图像显示了研究区域中的储层岩性的扩展。基于该研究的帕拉瓦形成作为潜在水库勘探的潜在水库结构来鉴定一个古社会。

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