###### Abstract  
The development of flow pattern identification technology for gas-liquid two-phase flow in porous media is of great significance to engineering research and production. In this paper, a high accuracy identification method for two-phase flow pattern in porous media is proposed with Machine learning techniques. The gas-liquid two-phase flow patterns and corresponding differential pressure signals in porous beds with particle diameters of 1.5 mm, 3 mm, and 6 mm are obtained through visual experiments. Three time domain characteristic parameters (Mean, Standard deviation, and Range) are calculated by a statistical method, while the EMD energy spectrum of the signal is obtained by empirical mode decomposition. Based on these parameters, machine learning technology, including support vector machine (SVM) and BP neural network, are employed to identify the flow pattern. Four flow pattern identification models are trained based on SVM and BP neural network, with accuracies of 94.77%, 93.4%, 96.08%, and 91.5%. Furthermore, the three models with good performance are integrated by integrated learning technology. An integrated identification model of gas-liquid two-phase flow pattern in porous media with an overall accuracy of 98.04% is finally obtained. flow patterns identification; porous media; two-phase flow; support vector machine; BP network; feature extraction +
Footnote †: journal:  
flow patterns timely and accurately [8]. This direct observation method is very effective for classifying and identifying typical flow patterns. However, it also has some limitations. For example, in real production, there are few transparent pipelines, which limits direct observation. In addition, for the transition flow pattern between typical flow patterns, it is difficult to distinguish which flow pattern it belongs to only through observation since it will be disturbed by the subjectivity of the observer. In order to solve the above problems, scholars try to improve the flow pattern identification method. Some scholars try to establish experimental systems in the laboratory to simulate the real situation. Using these devices, scholars have observed various flow patterns and obtained some corresponding signals that are easy to be measured, such as differential pressure signals [9; 10; 11], temperature signals [12], and so on. After extracting the features of these signals and classifying them according to the corresponding flow patterns, the relationship between different flow patterns and signal features can be established, which is the basis for flow pattern judgment. The signal feature extraction techniques can be divided into three categories: time domain feature extraction, frequency domain feature extraction, and time-frequency domain feature extraction. Time domain feature extraction includes mean value, variance, and probability density function (PDF), etc. The common method of frequency domain feature extraction is power spectral density (PSD). Time-frequency analysis technology appears late, including wavelet analysis, empirical mode decomposition (EMD), and Hilbert-Huang transform [13]. [PERSON] [14] calculated the probability density function (PDF) of the differential pressure signal and distinguished the flow pattern by the shape of the PDF curve. [PERSON] and [PERSON] [15] extracted the wavelet energy spectrums of differential pressure signals to distinguish the flow patterns. [PERSON] [16] summarized the characteristics of power spectral density (PSD) and PDF of the capacitive probe signal of slug flow. [PERSON] [17] identified the flow pattern in the dust collector through the synthesis of PSD, PDF, and wavelet features. [PERSON] [10] characterized different flow regimes and their transition in a concentric vertical annulus through autocorrelation, PSD, Shannon entropy, and permutation entropy. In recent years, machine learning technology has been developed in industries greatly [18; 19; 20; 21].