Image Fusion of ECT/ERT for Oil-Gas-Water Three-Phase Flow

Author(s):  
Lifeng Zhang

The tomographic imaging of process parameters for oil-gas-water three-phase flow can be obtained through different sensing modalities, such as electrical resistance tomography (ERT) and electrical capacitance tomography (ECT), both of which are sensitive to specific properties of the objects to be imaged. However, it is hard to discriminate oil, gas and water phases merely from reconstructed images of ERT or ECT. In this paper, the feasibility of image fusion based on ERT and ECT reconstructed images was investigated for oil-gas-water three-phase flow. Two cases were discussed and pixel-based image fusion method was presented. Simulation results showed that the cross-sectional reconstruction images of oil-gas-water three-phase flow can be obtained using the presented methods.

Author(s):  
Lifeng Zhang

The tomographic imaging of process parameters for oil-gas-water three-phase flow can be obtained through different sensing modalities, such as electrical resistance tomography (ERT) and electrical capacitance tomography (ECT), both of which are sensitive to specific properties of the objects to be imaged. However, it is hard to discriminate oil, gas and water phases merely from reconstructed images of ERT or ECT. In this paper, the feasibility of image fusion based on ERT and ECT reconstructed images was investigated for oil-gas-water three-phase flow. Two cases were discussed and pixel-based image fusion method was presented. Simulation results showed that the cross-sectional reconstruction images of oil-gas-water three-phase flow can be obtained using the presented methods.


2013 ◽  
Vol 483 ◽  
pp. 397-400
Author(s):  
Li Feng Zhang

Electrical resistance tomography (ERT) and electrical capacitance tomography (ECT) are sensitive to resistivity and permittivity distributions of the object to be imaged. However, it is hard to discriminate oil, gas and water phases only using ERT or ECT. In this paper, image fusion based on ERT and ECT reconstructed images was investigated for oil-gas-water three-phase flow. And then, pixel-based image fusion method was presented. Simulation results showed that the cross-sectional reconstruction images of oil-gas-water three-phase flow can be obtained using the presented methods for discussed cases.


2016 ◽  
Vol 71 (1) ◽  
pp. 33-43 ◽  
Author(s):  
An Zhao ◽  
Ning-de Jin ◽  
Ying-yu Ren ◽  
Lei Zhu ◽  
Xia Yang

AbstractIn 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.


2011 ◽  
Vol 199-200 ◽  
pp. 1609-1612
Author(s):  
Qian Jun Mao

It is well known that the oil-gas-water three-phase flow belongs to the field of multiphase flow,transfer heat mechanism of which is very complicated.Transfer heat mechanism is affected not only by different buries in oil gathering pipeline, but also by soil temperature periodicity change. Both domestic and oversea scholars have already studied on the transfer heat mechanisms of oil-gas-water three phase,but they are still in the level of fundamental theory and laboratory.This paper establishes transfer heat models of the oil-gas-water three-phase flow in buried oil gathering pipeline, including the physical model and the mathematical model,and testing in experiment .The purpose of this paper is to analyze value between the calculation and the testing . The results show that the mathematical model of this paper is accurate , and the relative error is ≤ 10%.


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