Measurement and analysis of water/oil multiphase flow using Electrical Capacitance Tomography sensor

Mohamad, Elmy Johana; Rahim, Ruzairi Abdul; Rahiman, Mohd Hafiz Fazalul; Ameran, Hanis Liyana Mohmad; Muji, Siti Zarina Mohd; Marwah, Omar Mohd Faizan

doi:10.1016/j.flowmeasinst.2015.12.004

Cited by 43 publications

(11 citation statements)

References 11 publications

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“…Reconstructing the ECT images involves solving forward and inverse problems [2]. Equation 1 solves the forward problem by estimating the electrical response u corresponding to known permittivity distribution ε, and the boundary electrical potential.…”

Section: Electrical Capacitance Tomography (Ect)mentioning

confidence: 99%

Efficient Image Reconstruction Algorithm for ECT System Using Local Ensemble Transform Kalman Filter

Deabes

Bouazza

2021

IEEE Access

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One of the vital processes that should be monitored and analyzed continuously in the oilgas and petroleum-related industries is the multi-phase flow inside pipes. Multi-phase flow means flowing two or more phases of gas, liquid, or solid inside a pipe. Electrical Capacitance Tomography (ECT) is a feasible and economical solution for monitoring dynamic applications. The ECT system offers the benefits of no radiation, non-intrusive, and non-invasive. Despite its potential, ECT systems deployment's major limitation is the crucial need to develop rapid image reconstruction algorithms. In this paper, a Local Ensemble Transform Kalman Filter (LETKF) is developed as a non-linear system estimator for reconstructing images in the ECT system. This method manages each node of the model independently by assimilating only the observations at a predefined distance. The localized approach of the LETKF gives it high computational efficiency allowing it to be applied to large dynamic systems. A quantitative analysis using Image Error (IE) and Coefficient Correlation (CC) measures has been applied to prove the effectiveness of the proposed algorithm. Indeed, the IE has been significantly decreased (around 62%), and the CC greatly increased (around 58%). Then, the influence of the noise was discussed. The results are promising and prove the algorithm feasibility.INDEX TERMS ECT, image reconstruction, Kalman filter, multi-phase flow.

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Section: Electrical Capacitance Tomography (Ect)mentioning

confidence: 99%

Efficient Image Reconstruction Algorithm for ECT System Using Local Ensemble Transform Kalman Filter

Deabes

Bouazza

2021

IEEE Access

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“…In [31], the authors went through a description of the various tomography technologies, such as resistive (ERT), capacitive (ECT), and magnetic (EMT) tomography, and described their applications, focusing particularly on capacitive tomography. ECT was also employed in [32], where the authors used this technology to take pictures of the flow distribution, which gave information on the flow regime and composition.…”

Section: Previous Workmentioning

confidence: 99%

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

et al. 2021

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In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred with other measurements by using soft sensor approaches that model the target quantity. For the purpose of production monitoring, point estimation alone is not enough, and a confidence interval is required in order to assess the uncertainty in the provided measure. Decisions based on these estimations can have a large impact on production costs; therefore, providing a quantification of uncertainty can help operators make the most correct choices. This paper focuses on the estimation of the performance indicator called the water-in-liquid ratio by using data-driven tools: firstly, anomaly detection techniques are employed to find data that can alter the performance of the subsequent model; then, different machine learning models, such as Gaussian processes, random forests, linear local forests, and neural networks, are tested and employed to perform uncertainty-aware predictions on data coming from an industrial tool, the multiphase flow meter, which collects multiple signals from the flow mixture. The reported results show the differences between the discussed approaches and the advantages of the uncertainty estimation; in particular, they show that methods such as the Gaussian process and linear local forest are capable of reaching competitive performance in terms of both RMSE (1.9–2.1) and estimated uncertainty (1.6–2.6).

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“…What is also important is that the algorithms of the analysis of signals which are used may also be applied in the analysis of data, e.g. in optical and electrical methods [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Prospects for the application of radiometric methods in the measurement of two-phase flows

Zych

2018

EPJ Web Conf.

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Abstract. The article constitutes an overview of the application of radiometric methods in the research of two-phase flows: liquid-solid particles and liquid-gas flows. The methods which were used were described on the basis of the experiments which were conducted in the Water Laboratory of the Wrocław University of Environmental and Life Sciences and in the Sedimentological Laboratory of the Faculty of Geology, Geophysics and Environmental Protection, AGH-UST in Kraków. The advanced mathematical methods for the analysis of signals from scintillation probes that were applied enable the acquisition of a number of parameters associated with the flowing two-phase mixture, such as: average velocities of the particular phases, concentration of the solid phase, and void fraction for a liquid-gas mixture. Despite the fact that the application of radioactive sources requires considerable carefulness and a number of state permits, in many cases these sources become useful in the experiments which are presented.

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Measurement and analysis of water/oil multiphase flow using Electrical Capacitance Tomography sensor

Cited by 43 publications

References 11 publications

Efficient Image Reconstruction Algorithm for ECT System Using Local Ensemble Transform Kalman Filter

Efficient Image Reconstruction Algorithm for ECT System Using Local Ensemble Transform Kalman Filter

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

Prospects for the application of radiometric methods in the measurement of two-phase flows

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