Spectral Noise Logging Data Processing Technology
- Yulia S. Maslennikova (TGT Prime) | V. V. Bochkarev (TGT Prime) | A. V. Savinkov (TGT Prime) | D. A. Davydov (TGT Prime)
- Document ID
- Society of Petroleum Engineers
- SPE Russian Oil and Gas Exploration and Production Technical Conference and Exhibition, 16-18 October, Moscow, Russia
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 2.2.2 Perforating, 5.6.1 Open hole/cased hole log analysis, 4.3.4 Scale, 4.2.3 Materials and Corrosion, 5.6.5 Tracers
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Rational development of oil and gas reservoirs is possible only with efficient monitoring by various well logging techniques. This paper presents algorithms for processing data acquired by spectral noise logging (SNL) in memory mode. The SNL technology is designed to identify flowing reservoir intervals, cross-flows behind casing and tubing and casing leaks by spectral analysis of recorded noise signals.
While moving through a reservoir, fluids and gases create turbulence and rock vibrations that in turn generate noise. This acoustic noise is recorded with a noise logging memory tool consisting of a high-sensitivity piezoelectric hydrophone sensor and an amplifier and data collection module. The tool records acoustic signals in the frequency range of 15 Hz to 60 kHz. The existing SNL technology excludes intense broadband noise created by the movement of the tool in the well.
Useful information is extracted from background noise using a technique based on wavelet thresholding. Spectral noise density in the depth-frequency plane undergoes a wavelet transform. At each measurement depth, several tens of noise signals are recorded to determine mean wavelet coefficients and their typical variance. Then, they are analysed to remove statistically insignificant details from the signal spectrum and to suppress noise components that are present throughout large depth intervals.
The processing of data acquired in tens of wells from various fields has show that the noise features identified by wavelet filtering correlate with open-hole data and are confirmed by conventional well logging techniques.
The concept of recording acoustic noise in wells dates as far back as 1955 when Enright (1955) qualitatively described the procedure of locating tubing and casing leaks with an acoustic recorder detecting the highest noise levels at leaks. Korotaev et al. (1970) used a noise detector to identify gas-bearing zones in uncased wells. Prof. McKinley (1994) introduced a noise logging technology based on the recording of acoustic noise into several frequency channels and presented the results of his own experimental research. Despite the ever-growing use of such tools, noise logging data were long considered untrustworthy and difficult to interpret. The successful development of precision downhole equipment lead to the creation of an acoustic noise logging tool with the piecewise continuous recording of acoustic noise and the spectral analysis of well noises in wide frequency ranges (Aslanyan 2010).
Qualitative and quantitative analyses of SNL data require filtering techniques that can extract statistically significant spectral components, while the useful signal is expected to be confined to a narrow permeable reservoir interval. This paper presents a modified method of wavelet thresholding to remove from the spectrum statistically insignificant features impeding qualitative interpretation. Data filtering for several wells is given as an example to illustrate the advantage of this algorithm over universal wavelet filtering techniques. It has also been shown that the noise features identified by filtering correlate with open-hole logging data and are confirmed by conventional well logging techniques.
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