Spectral moments and linear models used for photoacoustic detection of crude oil in produced water

Abstract

In this thesis, signal processing is performed on the output from the pulsed laser photoacoustic instrument monitoring crude oil in water. The instrument is constructed to perform inline monitoring of produced water in the pipeline during production. It is highly sensitive and testing was performed with hydrocarbons in water with concentrations in the range 0 - 1200 parts per million (ppm). The thesis discusses the basic theory behind photoacoustic, and the construction of the instrument. Data material acquired during testing of the instrument is explored to improve the accuracy of the instrument. The oil concentration is known to be affected by the following variables: photoacoustic response, temperature, salinity and pressure. These variables are analysed with statistical regression to show the instrument’s ability to calibrate a specific compound crude oil. Different methods of signal processing are used to enhance the result. When filtering, linear phase is necessary to avoid amplitude distortion of the peaks in the signal. This led to the use of a technique called spectral moments, a method that works directly on the Fourier spectrum and is insensible to phase. Statistics show that the spectral moments are able to enhance the result when equating the oil concentration. A new method for equating the oil concentration by filtering and arithmetic mean of the signal is discussed. The method is linked to the spectral moments with Parseval’s theorem, it is easy to implement and statistics show good performance. The thesis points out that the problems with a fouled instrument window must be solved to get the accuracy of the instrument down to the expected ± 100 ppm.