基于测井曲线小波分析与希尔伯特变换的流体识别新方法  

New fluid detection method based on well logging curve wavelet analysis and Hilbert Transform

作　　者：赵军龙[1] 李新胜 张彬 

机构地区：[1]西安石油大学油气资源学院,陕西西安710065 [2]陕西省地矿局物化探队,陕西西安710043 [3]延长油田有限责任公司定边采油厂,陕西定边718600

出　　处：《石油地球物理勘探》2010年第2期290-294,305,共6页Oil Geophysical Prospecting

基　　金：陕西省科学技术研究发展计划项目(工业攻关计划)(编号:2009K10-15)

摘　　要：低渗、特低渗及低阻油气等复杂储层的流体性质所对应的测井响应特征较为复杂,流体识别和评价的难度较大,多解性突出。本文以岩心、地质和动态资料为基础,从小波分析和希尔伯特变换原理出发,通过建立理论正演模型,对其测井响应进行小波分解与重构,选择能较好反映流体性质变化的趋势信号,对其开展希尔伯特变换,建立并分析趋势信号的振幅谱和相位谱,得出基于小波分解重构、希尔伯特变换的谱特征与流体性质关系。研究表明,常规测井曲线的小波重构信号可以更好地反映流体性质的纵向分布特征,小波重构测井曲线希尔伯特变换的振幅谱和相位谱可清晰地揭示流体性质的纵向变化及差异。将此方法应用于鄂尔多斯盆地,取得良好效果。The well logging response characteristics for fluid property in reservoirs with low and extra low permeability are extremely complicated, as a result it is very difficult to conduct fluid detection and evaluation for those reservoir and ambiguity is always a big problem. Based on available data (core data, geology data and dynamic data) and principles of wavelet analysis and Hilbert Transform, forward model was established, wavelet deposition and reconstruction were conducted, for trend information which could better reflect fluid property change, Hilbert transform was applied, then amplitude spectrum and phase spectrum for the trend signals were calculated and then were analyzed, then based on wavelet deposition and reconstruction, as well as Hilbert transform the relationship between spectrum characteristics and fluid property was obtained. The study results show that the wavelet reconstructed signal for the routine well logging curve could better reflect vertical distribution characteristics of the fluid properties, while the amplitude spectrum and phase spectrum of the wavelet reconstructed well logging curves for which Hilbert Transform was conducted could clearly disclose vertical change and difference for the fluid properties, the new method was applied in Erodes Basin, and excellent results ware achieved there.

关 键 词：复杂储层 测井曲线 小波分解与重构 希尔伯特变换 流体性质识别 

分 类 号：P618.13[天文地球—矿床学]