Methodology for production logging in oil-in-water flows under low flow rate and high water-cut conditions

This study aimed to obtain the production profiles of oil-in-water flow under low flow rate and high water-cut conditions in oil wells.A combination production profile logging composed of an arc-type conductance sensor(ATCS)and a cross-correlation flow meter(CFM)with a center body is proposed and experimentally evaluated.The ATCS is designed for water holdup measurement,whereas the CFM with a center body is proposed to obtain the mixture velocity.Then,a drift-flux model based on flow patterns is established to predict the individual-phase superficial velocity of oil-in-water flows.Results show that the ATCS possesses high resolution in water holdup measurement and that flow pattern information can be deduced from its signal through nonlinear time series analysis.The CFM can enhance the correlation of upstream and downstream signals and simplify the relationship between the cross-correlation velocity and mixture velocity.On the basis of the drift-flux model,individual-phase superficial velocities can be predicted with high accuracy for different flow patterns.

Multi-Scale Permutation Entropy:A Complexity Measure for Discriminating Two-Phase Flow Dynamics

We propose an improved permutation entropy method,i.e.,multi-scale permutation entropy(MSPE),for discrim-inating two-phase flow dynamics.We first take the signals from different typical dynamical systems as examples to demonstrate the effectiveness of the methods.In particular,we compute the MSPE values of sinusoidal signal,logistic,Lorenz and Chen chaotic signals and their signals with white Gaussian noise added.We find that the MSPE method can be an effective tool for analyzing the time series with distinct dynamics.We finally calculate the multi-scale permutation entropy and rate of MSPE from 66 groups of conductance fluctuating signals and find that these two measures can be used to identify different flow patterns and further explore dynamical char-acteristics of gas-liquid flow patterns.These results suggest that the MSPE can potentially be a useful tool for revealing the dynamical complexity of two-phase flow on different scales.

Local Property of Recurrence Network for Investigating Gas-Liquid Two-Phase Flow Characteristics

Understanding the dynamics of gas-liquid two-phase flows is a challenge in the fields of nonlinear dynamics.We first construct and analyze a recurrence network from Chen's chaotic system and find that the network local statistic is feasible to characterize chaotic dynamics associated with unstable periodic orbits.Then we construct recurrence networks from gas-liquid two-phase flow experimental signals and associate the network topological statistic with the flow pattern dynamics.The results indicate that the recurrence network could be a powerful tool for the dynamic characterization of experimental gas-liquid two-phase flows.