Identification of two-phase flow pattern in porous media based on signal feature extraction

The statistical analysis methods based on differential pressure signals of two-phase flow are employed in the present study to identify the flow patterns in packed porous bed. The typical flow pattern images of two-phase flow in the packed porous beds are recognized and the corresponding differential pressure signals are recorded based on the visualization experiments. Then the statistical analysis methods, including probability density function (PDF), power spectral density (PSD), and wavelet energy spectrum (WES), are employed to extract the features of differential pressure signals in the time domain, frequency domain, and time-frequency domain respectively. The dimensionless parameters are proposed as the evaluation index to quantify the differences among flow patterns. The results show that the PDF, PSD, and WES methods can effectively characterize different flow patterns in the time, frequency, and time-frequency domain, respectively. The comprehensive recognition efficiency is about 88.5% using the introduced dimensionless parameters.