Ning-De Jin

Multi-frequency complex network from time series for uncovering oil-water flow structure

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

A Four-Sector Conductance Method for Measuring and Characterizing Low-Velocity Oil–Water Two-Phase Flows

Measuring water holdup and characterizing the flow behavior of an oil-water two-phase flow is a contemporary and challenging problem of significant importance in industry. To address this problem, we develop a new method to design a new four-sector distributed conductance sensor. Specifically, we first use the finite-element method (FEM) to investigate the sensitivity distribution of the electric field and then calculate its response on the measurement electrodes. Based on the FEM analysis results, we extract two optimizing indexes to describe and find the optimum geometry for the four-sector distributed conductance sensor. We carry out oil-water two-phase flow experiments in a vertical upward pipe to validate the designed sensor implemented in the measurement of water holdup. In addition, we use the multivariate pseudo Wigner distribution (MPWD) method to analyze the multivariate signals from the four-sector distributed sensor. Our analytical and experimental results indicate that the four-sector distributed conductance sensor enables measuring water holdup and the MPWD allows uncovering local flow behavior revealing different oil-water flow patterns.

Design and geometry optimization of a conductivity probe with a vertical multiple electrode array for measuring volume fraction and axial velocity of two-phase flow

This paper presents the design and geometry optimization of a conductivity probe with a vertical multiple electrode array (VMEA), which can be used to measure the volume fraction and axial velocity of two-phase flow. The designed VMEA electrodes are axially flush mounted on the inside wall of an insulating duct. On the basis of a finite element analysis method, some new sensor optimization concepts of the electric field such as uniform degree, spatial sensitivity and effective information content are proposed. The designed VMEA measurement system has been tested through the multiphase flow loop and shows that the optimized VMEA can be used to measure cross-correlation velocity and predict volume fraction in vertical upward gas–water two-phase flow with satisfactory accuracy. The proposed optimization method of VMEA can also be useful in investigating other types of conductivity probes.

The development of a conductance method for measuring liquid holdup in horizontal oil?water two-phase flows

This paper presents the design and geometry optimization of a ring conductance probe for measuring the conductance of oil?water mixtures in horizontal pipes. Using the finite element method (FEM), we first investigate the sensitivity distribution of the electric field generated by a pair of ring-shaped exciting electrodes, and calculate the static response on the measurement electrodes under horizontal oil?water flow patterns; we then figure out the optimum geometry dimension with minimum deviation from linearity and high spatial resolution. Finally, we carry out the flow loop test in a horizontal oil?water two-phase flow pipe to obtain the measurement response of the ring conductance probe, and conclude some advantages for measuring liquid holdup with the newly designed conductance method.

Multivariate multiscale complex network analysis of vertical upward oil-water two-phase flow in a small diameter pipe.

High water cut and low velocity vertical upward oil-water two-phase flow is a typical complex system with the features of multiscale, unstable and non-homogenous. We first measure local flow information by using distributed conductance sensor and then develop a multivariate multiscale complex network (MMCN) to reveal the dispersed oil-in-water local flow behavior. Specifically, we infer complex networks at different scales from multi-channel measurements for three typical vertical oil-in-water flow patterns. Then we characterize the generated multiscale complex networks in terms of network clustering measure. The results suggest that the clustering coefficient entropy from the MMCN not only allows indicating the oil-in-water flow pattern transition but also enables to probe the dynamical flow behavior governing the transitions of vertical oil-water two-phase flow.

Multivariate weighted recurrence network inference for uncovering oil-water transitional flow behavior in a vertical pipe

Exploring the dynamical behaviors of high water cut and low velocity oil-water flows remains a contemporary and challenging problem of significant importance. This challenge stimulates us to design a high-speed cycle motivation conductance sensor to capture spatial local flow information. We systematically carry out experiments and acquire the multi-channel measurements from different oil-water flow patterns. Then we develop a novel multivariate weighted recurrence network for uncovering the flow behaviors from multi-channel measurements. In particular, we exploit graph energy and weighted clustering coefficient in combination with multivariate time-frequency analysis to characterize the derived complex networks. The results indicate that the network measures are very sensitive to the flow transitions and allow uncovering local dynamical behaviors associated with water cut and flow velocity. These properties render our method particularly useful for quantitatively characterizing dynamical behaviors governing the transition and evolution of different oil-water flow patterns.

The measurement of gas–liquid two-phase flows in a small diameter pipe using a dual-sensor multi-electrode conductance probe

We design a dual-sensor multi-electrode conductance probe to measure the flow parameters of gas–liquid two-phase flows in a vertical pipe with an inner diameter of 20 mm. The designed conductance probe consists of a phase volume fraction sensor (PVFS) and a cross-correlation velocity sensor (CCVS). Through inserting an insulated flow deflector in the central part of the pipe, the gas–liquid two-phase flows are forced to pass through an annual space. The multiple electrodes of the PVFS and the CCVS are flush-mounted on the inside of the pipe wall and the outside of the flow deflector, respectively. The geometry dimension of the PVFS is optimized based on the distribution characteristics of the sensor sensitivity field. In the flow loop test of vertical upward gas–liquid two-phase flows, the output signals from the dual-sensor multi-electrode conductance probe are collected by a data acquisition device from the National Instruments (NI) Corporation. The information transferring characteristics of local flow structures in the annular space are investigated using the transfer entropy theory. Additionally, the kinematic wave velocity is measured based on the drift velocity model to investigate the propagation behavior of the stable kinematic wave in the annular space. Finally, according to the motion characteristics of the gas–liquid two-phase flows, the drift velocity model based on the flow patterns is constructed to measure the individual phase flow rate with higher accuracy.

The Finite Element Analysis for Parallel-wire Capacitance Probe in Small Diameter Two-phase Flow Pipe

Abstract This paper presents a novel capacitance probe, i.e. , parallel-wire capacitance probe (PWCP), for two-phase flow measurement. Using finite element method (FEM), the sensitivity field of the PWCP is investigated and the optimum sensor geometry is determiend in term of the characterisitc parameters. Then, the response of PWCP for the oil-water stratified flow is calculated, and it is found the PWCP has better linearity and sensitivity to the variation of water-layer thickness, and is almost independant of the angle between the oil-water interface and the sensor electrode. Finally, the static experiment for oil-water stratified flow is carried out and the calibration method of liquid holdup is presented.

Testing for Nonlinearity in Dynamic Characteristics of Vertical Upward Oil-Gas-Water Three-phase Bubble and Slug Flows

Abstract Based on the conductance fluctuation signals measured from vertical upward oil-gas-water three-phase flow experiment, time frequency representation and surrogate data method were used to investigate dynamical characteristics of oil-in-water type bubble and slug flows. The results indicate that oil-in-water type bubble flow will turn to deterministic motion with the increase of oil phase fraction f o and superficial gas velocity U sg under fixed flowrate of oil-water mixture Q mix . The dynamics of oil-in-water type slug flow becomes more complex with the increase of U sg under fixed flowrate of oil-water mixture. The change of f o leads to irregular influence on the dynamics of slug flow. These interesting findings suggest that the surrogate data method can be a faithful tool for characterizing dynamic characteristics of oil-in-water type bubble and slug flows.

Multi-Scale Long-Range Magnitude and Sign Correlations in Vertical Upward Oil–Gas–Water Three-Phase Flow

Abstract In this article we apply an approach to identify the oil–gas–water three-phase flow patterns in vertical upwards 20 mm inner-diameter pipe based on the conductance fluctuating signals. We use the approach to analyse the signals with long-range correlations by decomposing the signal increment series into magnitude and sign series and extracting their scaling properties. We find that the magnitude series relates to nonlinear properties of the original time series, whereas the sign series relates to the linear properties. The research shows that the oil–gas–water three-phase flows (slug flow, churn flow, bubble flow) can be classified by a combination of scaling exponents of magnitude and sign series. This study provides a new way of characterising linear and nonlinear properties embedded in oil–gas–water three-phase flows.