Spectral Noise Logging Data Processing Technology

Abstract

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.

An Integrated Approach for Downhole Leak Detection

“…However, distributed temperature sensors are still not widely used and a lot of issues still exist regarding the complexity of the data analysis which is acting as a barrier to distributed temperature sensors usage. Currently, DTS (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) are used for evaluating the effectiveness of a stimulation job. The applications of temperature for problem diagnostics (8) Under normal conditions, this feature is relatively easier to detect.…”

Acoustic signal processing for well integrity monitoring systems

“…For example, Farooqui et al [14] introduced an acoustic-based tool, Johns et al [15] illustrated an ultrasonic method of leak detection for casing repair, and Johns et al [16] studied the attributes of ultrasound and leaks in Alaskan north slope wells. Other approaches were later based on noise measurement [17] and more recent integrated approaches [18], [19] in which noise and temperature measurements are coupled with a turbine or spinner flowmeter information to correlate data, which are then used to infer the location of leaks.…”

“…This implies that if the sampling rate of a sensor is set arbitrarily high, the sensor will be able to capture a wide range of frequencies. This is a promising feature for passive leak detection because a leak signal can span a wide range of frequencies, from 15 to 60 kHz [17]. However, a sensor array can be interpreted as a spatial sampling system that is limited by its spatial aliasing frequency.…”

“…A signal propagating through space and time is often corrupted by noise and interference [25]. A great deal of research has been conducted on this problem and focuses on detecting the presence of the desired signal and reducing the effect of noise when using a single sensor [17], [26], [27]. However, Johnson and Dudgeon [25] acknowledged that it is often advantageous to use an array of sensors for better performance.…”

“…to 60 kHz)[17]. The detection capability of such a sensor is limited by the Nyquist sampling theorem of 0.is the sensor sampling frequency.…”