AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors DOI Creative Commons
Abdulilah Mohammad Mayet, Salman Arafath Mohammed, Shamimul Qamar

et al.

ARO-The Scientific Journal of Koya University, Journal Year: 2024, Volume and Issue: 12(2), P. 167 - 178

Published: Nov. 9, 2024

Metering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors methods are developed to precisely measure volume fractions multi-phase fluids. A significant challenge fluid pipelines the formation of scale within pipes. This issue particularly problematic petroleum industry, leading narrowed internal diameters, corrosion, increased energy consumption, reduced equipment lifespan, and, most crucially, compromised flow paper proposes non-destructive metering system incorporating an artificial neural network with capacitive photon attenuation address this challenge. The simulates thicknesses from 0 mm 10 using COMSOL multiphysics software calculates counted rays through Beer Lambert equations. simulation considers 10% interval variation each phase, generating 726 data points. proposed network, two inputs—measured capacity rays-and three outputs—volume gas, water, oil—achieves mean absolute errors 0.318, 1.531, 1.614, respectively. These results demonstrate system’s ability accurately gauge proportions three-phase gas-water-oil fluid, regardless pipeline thickness.

Language: Английский

A novel metering system consists of capacitance-based sensor, gamma-ray sensor and ANN for measuring volume fractions of three-phase homogeneous flows DOI
Farhad Fouladinia, Seyed Mehdi Alizadeh,

Evgeniya Ilyinichna Gorelkina

et al.

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 27

Published: July 7, 2024

Measuring the volume fraction of different types fluids with two or three phases is so vital. Among all available methods, them, capacitance-based and gamma-ray attenuation, are popular widely used. Moreover, nowadays, AI which stands for Artificial Intelligence can be seen almost in areas, measuring section no exception. In this paper, main goal to predict a three-phase homogeneous fluid contains water, oil, gas materials. To opt an optimised method, combination sensors, attenuation sensor Neural Networks (ANN) utilised. train proposed metering system MLP type, inputs considered. For first input, concave simulated COMSOL Multiphysics software combinations (different fractions) applied. Then through theoretical investigations sensor, Barium-133 radiates 0.356 MeV This way, second required input generated. Finally, implement new accurate system, number networks characteristics run MATLAB software. The best structure had Mean Absolute Error (MAE) equal 0.33, 3.68 3.75 oil phases, respectively. accuracy presented illustrated by received outcomes. novelty study proposing combined method that measure fluid's fractions containing precisely.

Language: Английский

Citations

4
Low-flow measurement of oil–water two-phase flow based on the dynamic swirling differential pressure method DOI

Huixiong Wu,

Ruiquan Liao,

H L Qin

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(3)

Published: March 1, 2025

With the ongoing development of oilfield production, real-time monitoring wellbore flow rates has become a crucial indicator for evaluating efficiency. However, under low-flow conditions, sensitivity differential pressure is insufficient, and existing measurement methods are insufficient accurate conditions. To address this, this study introduces novel oil–water two-phase device based on dynamic spiral method. By applying external forces to swirling pipe section, irregular upstream forced into distinct “oil-core water-ring” flow, generating both axial radial pressures. The mechanisms behind these pressures analyzed, theoretical model developed. Thorough laboratory experiments examine relationships between dual rate, water cut at various rotational speeds, with experimental data used validate model. results indicate that method enhances measurements, rate positively correlated When speed exceeds 3000 rpm oil phase 0.7 m3/h, emulsification phases occurs, impacting accuracy. Experimental validation established reveals relative errors 4.69% 7.53%, respectively. effectively extends range using method, contributing advancement intelligent oilfields.

Language: Английский

Citations

0
MLP ANN Equipped Approach to Measuring Scale Layer in Oil-Gas-Water Homogeneous Fluid by Capacitive and Photon Attenuation Sensors DOI
Abdulilah Mohammad Mayet, Salman Arafath Mohammed,

Evgeniya Ilyinichna Gorelkina

et al.

Journal of Nondestructive Evaluation, Journal Year: 2025, Volume and Issue: 44(2)

Published: April 1, 2025

Language: Английский

Citations

0
Multiphase Flow’s Volume Fractions Intelligent Measurement by a Compound Method Employing Cesium-137, Photon Attenuation Sensor, and Capacitance-Based Sensor DOI Creative Commons
Abdulilah Mohammad Mayet, Farhad Fouladinia, Robert Hanus

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(14), P. 3519 - 3519

Published: July 18, 2024

Multiphase fluids are common in many industries, such as oil and petrochemical, volume fraction measurement of their phases is a vital subject. Hence, there lots scientists researchers who have introduced methods equipment this regard, for example, photon attenuation sensors, capacitance-based so on. These approaches non-invasive reason, very popular widely used. In addition, nowadays, artificial neural networks (ANN) attractive lot fields because accuracy. Therefore, paper, to estimate proportion three-phase homogeneous fluid, new system proposed that contains an MLP ANN, standing multilayer perceptron network, sensor, sensor. Through computational methods, capacities mass coefficients obtained, which act inputs the network. All these were divided randomly two main groups train test presented model. To opt suitable network with lowest rate mean absolute error (MAE), number architectures different factors tested MATLAB software R2023b. After receiving MAEs equal 0.29, 1.60, 1.67 water, gas, phases, respectively, was chosen be paper. based on outcomes, approach’s novelty being able predict all flow low error.

Language: Английский

Citations

1
A flow rate estimation method for gas–liquid two-phase flow based on filter-enhanced convolutional neural network DOI
Yuxiao Jiang, Yinyan Liu, Lihui Peng

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 139, P. 109593 - 109593

Published: Nov. 11, 2024

Language: Английский

Citations

1
AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors DOI Creative Commons
Abdulilah Mohammad Mayet, Salman Arafath Mohammed, Shamimul Qamar

et al.

ARO-The Scientific Journal of Koya University, Journal Year: 2024, Volume and Issue: 12(2), P. 167 - 178

Published: Nov. 9, 2024

Metering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors methods are developed to precisely measure volume fractions multi-phase fluids. A significant challenge fluid pipelines the formation of scale within pipes. This issue particularly problematic petroleum industry, leading narrowed internal diameters, corrosion, increased energy consumption, reduced equipment lifespan, and, most crucially, compromised flow paper proposes non-destructive metering system incorporating an artificial neural network with capacitive photon attenuation address this challenge. The simulates thicknesses from 0 mm 10 using COMSOL multiphysics software calculates counted rays through Beer Lambert equations. simulation considers 10% interval variation each phase, generating 726 data points. proposed network, two inputs—measured capacity rays-and three outputs—volume gas, water, oil—achieves mean absolute errors 0.318, 1.531, 1.614, respectively. These results demonstrate system’s ability accurately gauge proportions three-phase gas-water-oil fluid, regardless pipeline thickness.

Language: Английский

Citations

0