# An Intelligent Approach to Determine Component Volume Percentages in a Symmetrical Homogeneous Three-Phase Fluid in Scaled Pipe Conditions

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## Abstract

**:**

^{241}Am and

^{133}Ba radioisotopes. A three-phase flow consisting of oil, water, and gas exhibits symmetrical homogenous flow characteristics across varying volume percentages as it traverses through scaled pipes of varying thicknesses. It is worth mentioning that there is an axial symmetry of flow inside the pipe that creates a homogenous flow pattern. In this study, the experiment involved the emission of gamma rays from one end of a pipe, with photons being absorbed by two detectors located at the other end. The resulting data included three distinct features, namely the counts under the photopeaks of

^{241}Am and

^{133}Ba from the first detector as well as the total count from the second detector. Through the implementation of a two-output MLP neural network utilising the aforementioned inputs, it is possible to accurately forecast the volumetric percentages with an RMSE of under 1.22, regardless of the thickness of the scale. The minimal error value ensures the efficacy of the proposed technique and the practicality of its implementation in the domains of petroleum and petrochemicals.

## 1. Introduction

^{241}Am and

^{133}Ba from the first detector. The following are some of the contributions made by this study:

- It shows how to enhance the accuracy of the detection system.
- It is possible to obtain volumetric fraction measurements while a three-phase flow is passing through an oil pipeline in a homogenous flow regime, even in the presence of a scale layer.
- This study aims to investigate the efficacy of utilising the photopeaks of
^{241}Am and^{133}Ba in the first detector as well as the total count of the second detector for the purpose of determining volume percentages.

## 2. Simulation Setup

^{133}Ba and

^{241}Am form the backbone of our investigation. Photons from the previously described dual energy source had an energy of 59 keV and an energy of 356 keV. These photons were sent down a steel test pipe, where they were picked up by two detectors at the far end. These gamma rays emitted symmetrically from the source, and they were shaped using a shield. The two sodium iodide detectors used in this research were placed at angles of 0 and 7 degrees with respect to the theoretical horizon. Each detector measured exactly 2.54 cm × 2.54 cm. Within the test pipe itself, a homogenous flow regime was used to mimic a three-phase flow where the main phenomena took place. The aforementioned pipe was 10 cm in diameter and had a thickness of 0.5 cm. Inside this pipe was a thickness measuring instrument in the form of a BaSO

_{4}scale with several layers. There was a 4.5 g/cm

^{3}density scale within the pipe, and it came in 0 cm, 0.5 cm, 1 cm, 1.5 cm, 2 cm, and 2.5 cm widths and heights. The pipe could carry oil, water, and gas, and the scale was located inside of it. Water, gas, and oil were all modelled in this investigation, and their densities were 1, 0.00125, and 0.826 g per cubic centimetre, respectively. In this investigation, the simulation geometry was constructed throughout the MCNP code. The experimental framework used in this research served as the basis for validating the simulations [1]. The count totals obtained by the detectors in the experimental structure and the simulated structure were compared. It seems like they got along well enough to be considered a good match. Because there were 36 volume percentages for each of the 6 values of the scale thickness, the total number of simulations was 216. From each simulation, we extracted three separate features: the original detector’s count under photopeaks

^{241}Am and

^{133}Ba and the second detector’s overall count. The 3 × 216 feature matrix was used to train a neural network. The MLP neural network was trained using three introduced inputs to predict two outputs, including the volume percentages of gas and oil. In order to reduce the volume of calculations and increase the accuracy of the neural network, the volume percentage of water can be calculated separately by subtracting the volume of the two calculated products from the volume of the pipe. Figure 1 illustrates the complete structure as described. Figure 2 depicts the signals received from two detectors and extracted features.

## 3. Multilayer Perceptron Neural Network

## 4. Results

^{241}Am and

^{133}Ba photopeaks from the first detector as well as the total count from the second detector. In fact, the characteristics used in this study have been used in previous studies to determine other parameters of multiphase flows [45,46]. In this research, the performance of the mentioned characteristics to determine volume percentages in three-phase flows in the homogeneous regime and in the presence of the scale layer has been investigated. The high accuracy of the proposed method confirms the proposed characteristics in determining volume percentages. The percentages of oil and gas inside the pipe might then be predicted using the acquired features as inputs to a multi-layer perceptron neural network. Following training, the neural network’s output was checked against the target output to make sure it was producing the expected results.

## 5. Conclusions

^{241}Am and

^{133}Ba of the first detector and the overall count of the second detector. These characteristics were then used in the formation of a neural network. One MLP neural network was trained using the aforementioned characteristics as inputs, and the outputs of the neural network were the gas and oil volumes as percentages. Subtracting the amounts of oil and gas from the total volume of the pipe yields an accurate estimate of the water phase’s contribution to the pipe’s volume. The neural network’s promising performance in predicting the volume percentage with an RMSE of less than 1.22 stands out when compared to other studies. The suggested method’s excellent accuracy is attributable to its skillful feature extraction and subsequent use in training neural networks, which pave the way for the construction of optimal networks. The current study introduces a detection method that has been found to be highly useful and is strongly suggested for use in the oil and petroleum sectors.

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 4.**(

**a**) training, (

**b**) validation, and (

**c**) test data sets for a neural network that predicts a percentage of gas volumes.

**Figure 5.**(

**a**) training, (

**b**) validation, and (

**c**) test data sets for a neural network that predicts a percentage of oil volumes.

**Figure 6.**The overall process of the proposed method for measuring volume fractions within the pipe.

ANN | MLP | |||||
---|---|---|---|---|---|---|

Input layer neurons | 3 | |||||

Neurons in the 1st hidden layer | 20 | |||||

Neurons in the 2nd hidden layer | 15 | |||||

Neurons in the 3rd hidden layer | 5 | |||||

Neurons in the output layer | 2 | |||||

Epochs | 480 | |||||

Activation function | Tansig | |||||

Output | Gas | Oil | ||||

RMSE | Train | Validation | Test | Train | Validation | Test |

1.16 | 1.12 | 1.22 | 1.13 | 1.00 | 1.20 | |

MRE% | 4.79 | 3.84 | 5.46 | 4.54 | 4.91 | 4.82 |

Ref | Type of Neural Network | Extracted Features | Maximum RMSE | Maximum MSE |
---|---|---|---|---|

[1] | RBF | No feature extraction | 1.29 | 1.66 |

[32] | GMDH | No feature extraction | 2.71 | 7.34 |

[43] | MLP | No feature extraction | 4.13 | 17.05 |

[44] | MLP | No feature extraction | 1.6 | 2.56 |

[45] | RBF | Compton continuum and counts under full energy peaks of 1173 and 1333 keV | 6.12 | 37.45 |

[46] | MLP | No feature extraction | 2.12 | 4.49 |

[current study] | MLP | Photopeaks of ^{241}Am and ^{133}Ba in the first and total count of second detectors | 1.22 | 1.48 |

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## Share and Cite

**MDPI and ACS Style**

Mayet, A.M.; Alizadeh, S.M.; Ijyas, V.P.T.; Grimaldo Guerrero, J.W.; Shukla, N.K.; Bhutto, J.K.; Eftekhari-Zadeh, E.; Aiesh Qaisi, R.M.
An Intelligent Approach to Determine Component Volume Percentages in a Symmetrical Homogeneous Three-Phase Fluid in Scaled Pipe Conditions. *Symmetry* **2023**, *15*, 1131.
https://doi.org/10.3390/sym15061131

**AMA Style**

Mayet AM, Alizadeh SM, Ijyas VPT, Grimaldo Guerrero JW, Shukla NK, Bhutto JK, Eftekhari-Zadeh E, Aiesh Qaisi RM.
An Intelligent Approach to Determine Component Volume Percentages in a Symmetrical Homogeneous Three-Phase Fluid in Scaled Pipe Conditions. *Symmetry*. 2023; 15(6):1131.
https://doi.org/10.3390/sym15061131

**Chicago/Turabian Style**

Mayet, Abdulilah Mohammad, Seyed Mehdi Alizadeh, V. P. Thafasal Ijyas, John William Grimaldo Guerrero, Neeraj Kumar Shukla, Javed Khan Bhutto, Ehsan Eftekhari-Zadeh, and Ramy Mohammed Aiesh Qaisi.
2023. "An Intelligent Approach to Determine Component Volume Percentages in a Symmetrical Homogeneous Three-Phase Fluid in Scaled Pipe Conditions" *Symmetry* 15, no. 6: 1131.
https://doi.org/10.3390/sym15061131