Identifying Reservoir Fluids by Wavelet Transform of Well Logs

Abstract

Summary The wavelet-transform (WT) method has been applied to logs to extract reservoir-fluid information. In addition to the time (depth)/frequency analysis generally performed by the wavelet method, we also have performed energy spectral analysis for time/frequency-domain signals by the WT method. We have further developed a new method to identify reservoir fluid by setting up a correlation between the energy spectra and reservoir fluid. We have processed 42 models from an oil field in China using this method and have subsequently applied these rules to interpret reservoir layers. It is found that identifications by use of this method are in very good agreement with the results of well tests. Introduction An important log-analysis application is determining reservoir-fluid properties. It is common practice to calculate the water and oil saturations of reservoir formations by use of electrical logs. With the development of well-logging technology, a number of methods have been developed for reservoir-fluid typing with well logs (Hou 2002; Geng et al. 1983; Dahlberg and Ference 1984). A recent report has also described reservoir-fluid typing by the T2 differential spectrum from nuclear-magnetic-resonance (NMR) logs (Coates et al. 2001). However, because of the interference from vugs, fractures, clay content, and mud-filtrate invasion, the reservoir-fluid information contained in well logs is often concealed. The reliability of these log interpretations is thus limited in many cases. Therefore, it is desirable to find a more reliable and consistent way of reservoir-fluid typing with well logs. In this paper, we present a new method using the WT for fluid typing with well logs. The WT technique was developed with the localization idea from Gabor's short-time Fourier analysis and has been expanded further. Wavelets provide the ability to perform local analysis (i.e., analyze a small portion of a larger signal) (Daubechies 1992).This localized analysis represents the next logical step: a windowing technique with variable-sized regions. Wavelet analysis allows the use of long time intervals, where more-precise low-frequency information is wanted, and shorter intervals, where high-frequency information is needed. Wavelet analysis is capable of revealing aspects of data that other signal-analysis techniques miss: aspects such as trends, breakdown points, discontinuities in higher derivatives, and self-similarity. In well-logging-data processing, wavelet analysis has been used to identify formation boundaries, estimate reservoir parameters, and increase vertical resolution (Lu and Horne 2000; Panda et al. 1996; Jiao et al. 1999; Barchiesi and Gharbi 1999). For data interpretation, however, the identification of hydrocarbon-bearing zones by wavelet analysis is still under investigation. In this study, we have developed a technique of wavelet-energy-spectrum analysis (WESA) to identify reservoir-fluid types. We have applied this technique to field-data interpretation and have achieved very good results.