LSTM Model Based on Multi-Feature Extractor to Detect Flow Pattern Change Characteristics and Parameter Measurement

In the field of petroleum multiphase flow research, accurate measurement of oil-gas two-phase flow parameters is the research focus. This paper analyzes the changing characteristics of the flow pattern of oil-gas two-phase flow and measures the parameters. The experimental phenomenon shows that under different oil and gas flow rate, the flow patterns upstream and downstream of the venturi tube will change to varying degrees. The Convolutional Neural Network often used in the study of flow patterns, but the flow pattern recognition by the CNN model cannot consider the time series relationship. Therefore, this paper proposes a multi-feature extractor-based LSTM (MFE-LSTM) model to predict the parameters. First, the flow pattern features are extracted through the CNN model, and then the features extracted from different CNN models are combined to form a sentence. The relationship between different words in a sentence and different sentences is explored through the LSTM model. The MFE-LSTM algorithm analyzes the changing relationship between flow patterns with time series and spatial position. In addition, in this paper, the flow pattern horizontal splicing model and the flow pattern channel superposition model are designed. In this experiment, the distribution of oil flow rate is 1-
$10~ m^{3}/h $
, the distribution of gas flow rate is 20-
$150~ m^{3}/h $
, and the distribution of GVF (gas void fraction) is 0.25-0.95. The experimental results show that the MFE-LSTM model has the best prediction effect. The average prediction relative error of gas flow rate is 0.387%. The average prediction relative error of oil flow rate is 2.081%.