Two-phase flow patterns identification in porous media using feature extraction and SVM

Abstract

• Support vector machine is employed to identify the flow patterns in packed beds. • Features of different flow patterns are extracted based on different pressure signals. • Feature extraction is conducted by the PDF, PSD and WES methods respectively. • Three SVM models are trained and their identification ability is compared. • Identification rate of typical flow patterns is up to 96.08%. Rapid and accurate identification of two-phase flow patterns in porous media is of great significance to the chemical industry, petroleum and nuclear engineering, etc. Based on the different pressure signals of gas-liquid two-phase flow in a porous bed, the present work proposes an intelligent recognition method to identify the two-phase flow patterns in porous media by the technologies of feature extraction and support vector machine (SVM). The analysis techniques, including time domain (PDF), frequency domain (PSD) and time-frequency domain (Wavelet), are employed to extract and summarize the corresponding characteristics of differential pressure signals of flow patterns. The intelligent recognition models are developed to identify the two-phase flow patterns in porous media by SVM. The models are trained respectively based on the characteristics of time domain + frequency domain (TF-SVM model), time domain + wavelet (TW-SVM model) and frequency domain + wavelet (FW-SVM model). The overall identification accuracy of the optimal model (TW-SVM model) reaches 96.08%.