井附近慢速地层s波速度剖面全波形反演方法

0 ENERGY & FUELS
Chao Zhang , Hui Wang , Da Chen , Wei Guan , Xiaodong He
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引用次数: 0

摘要

井眼全波形反演(BFWI)方法是一种利用声波测井波形提供的综合信息反演慢地层s波速度的新方法。该方法与传统技术不同,传统技术仅限于在测井工具的轴向分辨率范围内具有均匀速度的地层,或者已知s波速度的径向分布模式,并且只能利用声波波形数据的一部分。BFWI方法通过利用包括导波和临界折射波在内的完整波形信息,迭代地增强模型,使反演波形与实际波形对齐。这种对数据的综合利用大大提高了缓慢地层中估计横波速度的精度和可靠性。该方法的有效性已通过薄多层和蚀变层模型的测试得到验证,表明BFWI在电缆测井和随钻测井两种情况下都能准确描述s波速度剖面。此外,在超慢速地层中,即使存在干扰性泄漏纵波,BFWI不仅可以准确地反演s波速度剖面,还可以高精度地反演p波速度。这一进步为更精确地测量慢速地层的s波速度剖面提供了一个强大的工具,有可能提高石油工业地下特征的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A full waveform inversion method for inverting S-wave velocity profiles of slow formations near borehole
The borehole full waveform inversion (BFWI) method represents a novel approach for inverting S-wave velocities in slow formations by utilizing the comprehensive information available from acoustic logging waveforms. This method stands out from traditional techniques, which are limited to formations with uniform velocity within the axial resolution of the logging tool or where the radial distribution pattern of S-wave velocity is already known, and which only make use of a subset of the acoustic waveform data. The BFWI method iteratively enhances the model to align the inverted waveforms with the actual waveforms by leveraging the full waveform information, including both guided and critically refracted waves. This comprehensive use of data significantly improves the precision and reliability of estimating S-wave velocities in slow formations. The method’s effectiveness has been demonstrated through tests on thin-multilayer and alteration-zone models, showing that BFWI can accurately depict the S-wave velocity profiles in both wireline logging and logging-while-drilling scenarios. Moreover, within ultra-slow formations, BFWI not only accurately inverts the S-wave velocity profile but also retrieves the P-wave velocity with high precision, even in the presence of interfering leakage P-waves. This advancement offers a robust tool for the more precise measurement of S-wave velocity profiles in slow formations, potentially enhancing the accuracy of subsurface characterization in the petroleum industry.
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