Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries

Abstract

Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

“…For example, in [4], the best features are selected from an innovative method by measuring the distance between different classes. In papers [7,10], from the signals of approximation and detail extracted by wavelet transform, another step of time characteristic extraction is performed. In study [28], using correlation analysis, the characteristics that have the least similarity with other characteristics were selected as effective characteristics.…”

“…Failure to use characteristic extraction techniques in these studies prevented access to high accuracy. Researchers examined a large number of time characteristics [4,5], frequency characteristics [6], and wavelet features [7] to determine the type of flow regimes and volume percentages. Although the accuracy of their proposed system was appropriate, non-consideration of scale deposited inside the pipe has been noted from research gaps.…”

Accurate Flow Regime Classification and Void Fraction Measurement in Two-Phase Flowmeters Using Frequency-Domain Feature Extraction and Neural Networks

“…Gasoil and air were considered as the liquid and gas phases, respectively. A 137 Cs source and two NaI detectors In recent years, many researchers have put a great deal of effort into oil and gas fields for flow regime identification and void fraction measurement by utilizing different methods such as GMDH and wavelet feature extraction [16][17][18].…”

“…Different features in the frequency domain were presented by Hanus and co-workers in order to identify the flow regimes in a dynamic condition [10]. In recent years, many researchers have put a great deal of effort into oil and gas fields for flow regime identification and void fraction measurement by utilizing different methods such as GMDH and wavelet feature extraction [16][17][18].…”

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

“…Researchers have claimed that the feature extraction techniques can help increase the accuracy of determining the type of flow regimes. In their study, Hosseini et al [26] simulated a laboratory structure with an MCNP transport code in which they implemented three homogeneous, stratified, and annular regimes in different volume percentages. A cesium-137 source and a NaI detector were used in this simulation.…”