Electrical Tomography: A Review of Configurations, and Application to Fibre Flow Suspensions Characterisation

Prediction of fluids volume fraction and barium sulfate scale in a multiphase system using gamma radiation and deep neural network

In the oil industry, during the production of oil and gas, barium sulfate (BaSO4) scale may occur on the inner walls of the pipelines leading to the reduction of the internal diameter, making the fluids' passage difficult and complicating the calculation of the fluids volume fraction. This paper presents a methodology to predict volume fraction of fluids and BaSO4 scale thickness from obtaining spectra of two NaI(Tl) detectors that record the transmitted and scattered beams of gamma-rays. Theoretical models for a multiphase annular flow regime (gas-saltwater-oil-scale) were developed using MCNP6 code, which is a mathematical code based on the Monte Carlo method. The simulated data was used to train a deep neural network (DNN) to predict the volume fraction of gas, saltwater and oil, and the concentric scale thickness. A Python optimization library called Optuna was used for the hyperparameters search to design the DNN architecture. The methodology presented great results, especially for scale thickness prediction. Although the results for saltwater did not reach the same level, it was still possible to predict approximately 70% of the patterns up to 10% relative error. This achievement indicates the possibility to calculate the volume fraction of fluids and the concentric scale thickness in the offshore oil industry using gamma densitometry and deep learning models.

A new process analytical technology soft sensor based on electrical tomography for real-time monitoring of multiphase systems

BACKGROUND

Electrical tomography is widely recognized for its high time resolution and low cost. However, the implementation of electrical tomographic solutions has been hindered by the high computational overhead associated, which causes delays in the analysis, and numerical instability, that results in unclear reconstructed images. Therefore, it has been mostly applied offline, for qualitative tasks and with some delay. Applications requiring fast response times and quantification have been hindered or ruled out.

RESULTS

In this article, we propose a new process analytical technology soft sensor that maps directly electrical tomography signals to the relevant parameter to be monitored. The data acquisition and estimation steps occur almost instantaneously, and the final accuracy is very good (R2 = 0,994).

SIGNIFICANCE AND NOVELTY

The proposed methodology opens up good prospects for real-time quantitative applications. It was successfully tested on a pilot piping installation where the target property is the interface height between two immiscible fluids.

Fuzzy Regulator for Two-Phase Gas–Liquid Pipe Flows Control

The paper presents an intelligent module to control dynamic two-phase gas–liquid mixtures pipelines flow processes. The module is intelligent because it uses the algorithm based on AI methods, namely, fuzzy logic inference, to build the fuzzy regulator concept. The developed modification has allowed to design and implement the black-box type regulator. Therefore, it is not required to determine any of the complicated computer models of the flow rig, which is unfortunately necessary when using the classic regulators. The inputs of the regulator are four linguistic variables that are decomposed into two classes and two methods of fuzzification. The first input class describes the current values of gas and liquid pipe flows, which at the same time are the controlled values manipulated to generate desired flow type. The second class of the input signals contains a current flow state, namely, its name and the name preferred by the operator flow type. This approach improves the control accuracy since the given flow type can be generated with different gas and liquid volume fractions. Those values can be optimized by knowing the current flow type. Moreover, the fuzzification algorithm used for the input signals included in the first-class covers the current crisp signal value and its trend making the inference more accurate and resistant to slight measurement system inaccuracy. This approach of defined input signals in such environments is used for the first time. Considering all mentioned methods, it is possible to generate the desired flow type by manipulating the system input signals by minimum required values. Furthermore, a flow type can be changed by adjusting only one of the input signals. As an output of the inference process, two linguistic values are received, which are fuzzified adjustment values of the liquid pump and gas flow meter. The regulator looks to be universal, and it can be adopted by multiple test and production rigs. Moreover, once configured with a dedicated rig, it can be easily operated by the non (domain) technical staff. The usage of fuzzy terms makes understanding both the control strategy working principles and the obtained results easy.

Measurement-Based Domain Parameter Optimization in Electrical Impedance Tomography Imaging

This paper discusses the optimization of domain parameters in electrical impedance tomography-based imaging. Precise image reconstruction requires accurate, well-correlated physical and numerical finite element method (FEM) models; thus, we employed the Nelder–Mead algorithm and a complete electrode model to evaluate the individual parameters, including the initial conductivity, electrode misplacement, and shape deformation. The optimization process was designed to calculate the parameters of the numerical model before the image reconstruction. The models were verified via simulation and experimental measurement with single source current patterns. The impact of the optimization on the above parameters was reflected in the applied image reconstruction process, where the conductivity error dropped by 6.16% and 11.58% in adjacent and opposite driving, respectively. In the shape deformation, the inhomogeneity area ratio increased by 11.0% and 48.9%; the imprecise placement of the 6th electrode was successfully optimized with adjacent driving; the conductivity error dropped by 12.69%; and the inhomogeneity localization exhibited a rise of 66.7%. The opposite driving option produces undesired duality resulting from the measurement pattern. The designed optimization process proved to be suitable for correlating the numerical and the physical models, and it also enabled us to eliminate imaging uncertainties and artifacts.