A Novel Deep Learning Framework for Industrial Multiphase Flow Characterization

Due to the inherent disturbances associated with flow structures, measurement of the complicated flow parameters in multiphase flows remains a challenging problem of significant importance. The flow dynamical behaviors are still elusive. In this paper, a multichannel complex impedance measurement system is designed to cope with this difficult issue. First, the geometry of the distributed multielectrode impedance sensor is optimized and a matched hardware measurement system is developed. After performance evaluation, a convolutional neural network and long short-term memory based measurement model is formulated for measuring flow parameters with high accuracy. The mean absolute error is only 0.36% for water cut and 0.77% for total flow velocity. Further, from the perspective of Lempel–Ziv complexity and mutual information, the relationship between the diverse flow structures and spatial flow behaviors is explored, leading to a deeper understanding of oil-water flows. All the experimental and analytical results demonstrate that the combination of deep learning and the designed impedance sensor measurement system allows measuring the complicated flow parameters, thereby characterizing the flow structures and behaviors. This opens up a new venue for exploring industrial multiphase flows and serving for an efficient oilfield exploitation as well.

[1]  Oscar Mauricio Hernandez Rodriguez,et al.  Applications of wire-mesh sensors in multiphase flows , 2015 .

[2]  Yuxuan Yang,et al.  A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System , 2018, IEEE Transactions on Industrial Informatics.

[3]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[4]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[5]  R. Shirley,et al.  ARTIFICIAL NEURAL NETWORKS IN LIQUID-LIQUID TWO-PHASE FLOW , 2012 .

[6]  Zhi-Hong Mao,et al.  Deep learning for classification of normal swallows in adults , 2018, Neurocomputing.

[7]  Steve Renals,et al.  Convolutional Neural Networks for Distant Speech Recognition , 2014, IEEE Signal Processing Letters.

[8]  Ali Emadi,et al.  Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[9]  Jiheon Kang,et al.  Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems , 2018, IEEE Transactions on Industrial Electronics.

[10]  Yinhai Wang,et al.  Dynamic analysis of traffic time series at different temporal scales: A complex networks approach , 2014 .

[11]  Fernando L. Teixeira,et al.  Velocity Profiling of Multiphase Flows Using Capacitive Sensor Sensitivity Gradient , 2016, IEEE Sensors Journal.

[12]  M. Villar,et al.  Characterization of Concrete by Calibrating Thermo-Hydraulic Multiphase Flow Models , 2015, Transport in Porous Media.

[13]  Wei-Dong Dang,et al.  Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior. , 2017, Chaos.

[14]  Xinmin Wang,et al.  EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[15]  K. Jensen,et al.  Oscillatory multiphase flow strategy for chemistry and biology. , 2016, Lab on a chip.

[16]  Guanrong Chen,et al.  A Four-Sector Conductance Method for Measuring and Characterizing Low-Velocity Oil–Water Two-Phase Flows , 2016, IEEE Transactions on Instrumentation and Measurement.

[17]  Arvin Ebrahimkhanlou,et al.  Single-sensor acoustic emission source localization in plate-like structures: a deep learning approach , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[18]  A. Dastranj,et al.  A Capacitance Sensor for Gas/Oil Two-Phase Flow Measurement: Exciting Frequency Analysis and Static Experiment , 2017, IEEE Sensors Journal.

[19]  Sergey I. Nikolenko,et al.  Exploring convolutional neural networks and topic models for user profiling from drug reviews , 2017, Multimedia Tools and Applications.

[20]  Tomasz Piasecki,et al.  Simple Wide Frequency Range Impedance Meter Based on AD5933 Integrated Circuit , 2015 .

[21]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[22]  Yi Li,et al.  Measurement of water content of oil-water two-phase flows using dual-frequency microwave method in combination with deep neural network , 2019, Measurement.

[23]  Weiwei Liu,et al.  An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images , 2018, Scientific Reports.

[24]  Zhongke Gao,et al.  Multilayer Network from Multivariate Time Series for Characterizing Nonlinear Flow Behavior , 2017, Int. J. Bifurc. Chaos.

[25]  Farouq S. Mjalli,et al.  PREDICTION OF HORIZONTAL OIL-WATER FLOW PRESSURE GRADIENT USING ARTIFICIAL INTELLIGENCE TECHNIQUES , 2014 .

[26]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[27]  Lijun Xie,et al.  A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data , 2018, Pattern Recognit..

[28]  Jianping Fan,et al.  Integrating multi-level deep learning and concept ontology for large-scale visual recognition , 2018, Pattern Recognit..

[29]  Houshang Darabi,et al.  LSTM Fully Convolutional Networks for Time Series Classification , 2017, IEEE Access.

[30]  Zhiyao Huang,et al.  A new contactless impedance sensor for void fraction measurement of gas–liquid two-phase flow , 2016 .

[31]  H. Afarideh,et al.  Determination of oil–water volume fraction by using a pencil-beam collimated gamma-ray emitting source in a homogenized flow regime condition , 2016 .

[32]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[33]  S. Azizi,et al.  Prediction of water holdup in vertical and inclined oil–water two-phase flow using artificial neural network , 2016 .

[34]  Yinan Kong,et al.  Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering , 2018, BioMed research international.

[35]  Peter E.D. Love,et al.  A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory , 2018 .

[36]  Taimoor Asim,et al.  Optimal design of a four-sensor probe system to measure the flow properties of the dispersed phase in bubbly air–water multiphase flows , 2013 .

[37]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[38]  E S Rosa,et al.  Design and performance prediction of an impedance void meter applied to the petroleum industry , 2012 .

[39]  Subhransu Maji,et al.  Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  U. Rajendra Acharya,et al.  Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals , 2018, Comput. Biol. Medicine.

[41]  Yousef Faraj,et al.  Measurement of Vertical Oil-in-water Two-phase Flow Using Dual-modality ERT-EMF System , 2015 .

[42]  S. Tavoularis,et al.  On the accuracy of gas flow rate measurements in gas–liquid pipe flows by cross-correlating dual wire-mesh sensor signals , 2016 .

[43]  Ning-De Jin,et al.  Effects of flow patterns and salinity on water holdup measurement of oil-water two-phase flow using a conductance method , 2016 .

[44]  Xue Wang,et al.  Application of soft computing techniques to multiphase flow measurement: A review , 2018 .