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Dam RSDF, Dos Santos MC, Salgado WL, da Cruz BL, Schirru R, Salgado CM. Prediction of fluids volume fraction and barium sulfate scale in a multiphase system using gamma radiation and deep neural network. Appl Radiat Isot 2023; 201:111021. [PMID: 37699325 DOI: 10.1016/j.apradiso.2023.111021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/22/2023] [Accepted: 09/05/2023] [Indexed: 09/14/2023]
Abstract
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.
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Affiliation(s)
- Roos Sophia de Freitas Dam
- Programa de Engenharia Nuclear, Universidade Federal do Rio de Janeiro, Avenida Horácio de Macedo 2030, Bloco G - Sala 206, Zip Code 21941-914, Cidade Universitária, RJ, Brazil; Instituto de Engenharia Nuclear, Rua Hélio de Almeida 75, Zip Code 21941-906, Cidade Universitária, RJ, Brazil.
| | - Marcelo Carvalho Dos Santos
- Programa de Engenharia Nuclear, Universidade Federal do Rio de Janeiro, Avenida Horácio de Macedo 2030, Bloco G - Sala 206, Zip Code 21941-914, Cidade Universitária, RJ, Brazil.
| | - William Luna Salgado
- Instituto de Engenharia Nuclear, Rua Hélio de Almeida 75, Zip Code 21941-906, Cidade Universitária, RJ, Brazil.
| | - Bianca Lamarca da Cruz
- Instituto de Engenharia Nuclear, Rua Hélio de Almeida 75, Zip Code 21941-906, Cidade Universitária, RJ, Brazil.
| | - Roberto Schirru
- Programa de Engenharia Nuclear, Universidade Federal do Rio de Janeiro, Avenida Horácio de Macedo 2030, Bloco G - Sala 206, Zip Code 21941-914, Cidade Universitária, RJ, Brazil.
| | - César Marques Salgado
- Instituto de Engenharia Nuclear, Rua Hélio de Almeida 75, Zip Code 21941-906, Cidade Universitária, RJ, Brazil.
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Salgado WL, Dam RSDF, Desterro FSMD, Cruz BLD, Silva AXD, Salgado CM. Application of deep neural network and gamma radiation to monitor the transport of petroleum by-products through polyducts. Appl Radiat Isot 2023; 200:110973. [PMID: 37586248 DOI: 10.1016/j.apradiso.2023.110973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/29/2023] [Accepted: 08/08/2023] [Indexed: 08/18/2023]
Abstract
To continuously monitor information about the transport of fluids by sequential batches in polyduct, found in the petrochemical industry, it is necessary to manage the mixing zone - transmix - that occurs when two fluids are being transported. This scenario demonstrates the need to estimate the interface region and the purity of the fluids in this region to improve the management of the pipeline and, thus, reduce associated costs. This study presents a measurement system based on the dual-modality gamma densitometry technique in combination with a deep neural network with seven hidden layers to predict the purity level of four different fluids (Gasoline, Glycerol, Kerosene and Oil Fuel) in the transmix. The detection geometry is composed of a137Cs radioactive source (emitting gamma rays of 661.657 keV) and two NaI(Tl) scintillator detectors to record the transmitted and scattered photons. The study was performed by computer simulations using the MCNP6 code, and the information recorded in the detectors was used as input data for training and evaluating the deep neural network. The proposed intelligent measurement system is able to predict the purity level of fluids with errors with mean squared error values below 1.4 and mean absolute percentage error values below 5.73% for all analyzed data.
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Affiliation(s)
- William Luna Salgado
- Divisão de Radiofármacos (DIRA), Instituto de Engenharia Nuclear - (IEN), Rua Hélio de Almeida 75, 21941-906, Cidade Universitária, RJ, Brazil.
| | - Roos Sophia de Freitas Dam
- Divisão de Radiofármacos (DIRA), Instituto de Engenharia Nuclear - (IEN), Rua Hélio de Almeida 75, 21941-906, Cidade Universitária, RJ, Brazil; Programa de Engenharia Nuclear - (PEN/COPPE), Universidade Federal do Rio de Janeiro - (UFRJ), Avenida Horácio de Macedo 2030, G - 206, 21941-914, Cidade Universitária, RJ, Brazil.
| | - Filipe Santana Moreira do Desterro
- Programa de Engenharia Nuclear - (PEN/COPPE), Universidade Federal do Rio de Janeiro - (UFRJ), Avenida Horácio de Macedo 2030, G - 206, 21941-914, Cidade Universitária, RJ, Brazil.
| | - Bianca Lamarca da Cruz
- Divisão de Radiofármacos (DIRA), Instituto de Engenharia Nuclear - (IEN), Rua Hélio de Almeida 75, 21941-906, Cidade Universitária, RJ, Brazil.
| | - Ademir Xavier da Silva
- Programa de Engenharia Nuclear - (PEN/COPPE), Universidade Federal do Rio de Janeiro - (UFRJ), Avenida Horácio de Macedo 2030, G - 206, 21941-914, Cidade Universitária, RJ, Brazil.
| | - César Marques Salgado
- Divisão de Radiofármacos (DIRA), Instituto de Engenharia Nuclear - (IEN), Rua Hélio de Almeida 75, 21941-906, Cidade Universitária, RJ, Brazil.
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Menezes dos Santos AG, de Freitas Dam RS, da Cruz PAL, Salgado WL, Salgado CM. Thickness prediction in metal alloys using nuclear techniques and artificial neural network: Modelling. Appl Radiat Isot 2023; 191:110531. [DOI: 10.1016/j.apradiso.2022.110531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 11/02/2022]
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Application of deep neural network and gamma-ray scattering in eccentric scale calculation regardless of the fluids volume fraction inside a pipeline. Appl Radiat Isot 2022; 188:110353. [DOI: 10.1016/j.apradiso.2022.110353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 06/24/2022] [Accepted: 06/26/2022] [Indexed: 11/23/2022]
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Salgado CM, Dam RS, Puertas EJ, Salgado WL. Calculation of volume fractions regardless scale deposition in the oil industry pipelines using feed-forward multilayer perceptron artificial neural network and MCNP6 code. Appl Radiat Isot 2022; 185:110215. [DOI: 10.1016/j.apradiso.2022.110215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 12/09/2021] [Accepted: 03/24/2022] [Indexed: 11/02/2022]
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Salgado W, Dam R, Puertas E, Salgado C, Silva A. Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products. Appl Radiat Isot 2022; 186:110267. [DOI: 10.1016/j.apradiso.2022.110267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/08/2022] [Accepted: 04/28/2022] [Indexed: 11/26/2022]
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Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems. ENERGIES 2022. [DOI: 10.3390/en15061986] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.
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Salgado W, Dam R, Salgado C, Silva A. Identification of the interface region in transport of petroleum by-products in polyducts using artificial neural network and gamma densitometry by the MCNPX code. Radiat Phys Chem Oxf Engl 1993 2022. [DOI: 10.1016/j.radphyschem.2021.109908] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Application of radioactive particle tracking and an artificial neural network to calculating the flow rate in a two-phase (oil-water) stratified flow regime. Appl Radiat Isot 2021; 180:110061. [PMID: 34906851 DOI: 10.1016/j.apradiso.2021.110061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/09/2021] [Accepted: 12/06/2021] [Indexed: 11/21/2022]
Abstract
A multiphase flow is defined as the transport of two or more fluids with different properties flowing together inside a pipeline. After offshore oil production, it is necessary to control the amount of transported fluids based on flow rate measurements. Therefore, in this study, we developed a simulation method for predicting the volume fraction and calculating the superficial velocity for a two-phase flow based on radioactive particle tracking, which involves using a sealed radiation source inside the pipeline in order to obtain volume fraction measurements. The test section for the multiphase flow comprised oil and saltwater under a stratified flow regime, with a polyvinyl chloride pipe, four NaI(Tl) detectors, and a137Cs radioactive particle that emitted gamma-rays at 662 keV. Simulations were conducted using the MCNP6 code, which is a mathematical code based on the Monte Carlo method. Volume fraction predictions were obtained using a multilayer perceptron neural network with a backpropagation algorithm. The novel feature of this method is the combination of radioactive particle tracking with an artificial neural network in order to predict volume fractions in multiphase flows. The results showed that 91.65% of the predicted patterns were within 5% of the relative error. In addition, the time delay was determined using the cross-correlation function to obtain the superficial velocity in three different volume fractions, which allowed each phase flow rate to be calculated in these cases.
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Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products. MATHEMATICS 2021. [DOI: 10.3390/math9243215] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.
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El Hamli A, Bazza A, Moussa A, Hamal M, Zerfaoui M, Ouchrif M. Data and simulation studies on the influence of scintillation crystal dimensions on spectrometric parameters. Appl Radiat Isot 2021; 181:110053. [DOI: 10.1016/j.apradiso.2021.110053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/22/2021] [Accepted: 11/27/2021] [Indexed: 11/02/2022]
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Salgado CM, Dam RS, Salgado WL, Santos MC, Schirru R. Development of a deep rectifier neural network for fluid volume fraction prediction in multiphase flows by gamma-ray densitometry. Radiat Phys Chem Oxf Engl 1993 2021. [DOI: 10.1016/j.radphyschem.2021.109708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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A novel radioactive particle tracking algorithm based on deep rectifier neural network. NUCLEAR ENGINEERING AND TECHNOLOGY 2021. [DOI: 10.1016/j.net.2021.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Development of a non-invasive method for monitoring variations in salt concentrations of seawater using nuclear technique and Monte Carlo simulation. Appl Radiat Isot 2021; 174:109784. [PMID: 34087688 DOI: 10.1016/j.apradiso.2021.109784] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 12/23/2020] [Accepted: 05/13/2021] [Indexed: 11/21/2022]
Abstract
In the oil production industry, water is used as a fluid injected into the well to raise the oil when the well is depressurized. Water thus produced presents variations in the concentrations of dissolved salts, as there is a mixture of different types of water, related to its origin (such as connate water, sea water). Because it is reused in oil production, water needs to be monitored to maintain the standard suitable for its use as it can be hypersaline, contributing to the encrustation of pipes and contamination of underground water reservoirs. In this study, a noninvasive method was developed to determine the salt concentration in seawater. The method uses a detection system that contains a NaI(Tl) detector, a241Am source, and a sample holder to measure the mass attenuation coefficient of saltwater samples. For validation, the same setup was also simulated using the MCNPX code. Saltwater samples with different concentrations of NaCl and KBr were used as a proxy for seawater. The mass attenuation coefficients for the simulation exhibited the smallest relative errors (up to 6.2%), and the experimental ones exhibited the highest relative errors (up to 25%) when compared with theoretical values.
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Simulation Study of Utilizing X-ray Tube in Monitoring Systems of Liquid Petroleum Products. Processes (Basel) 2021. [DOI: 10.3390/pr9050828] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources.
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Roshani M, Phan G, Faraj RH, Phan NH, Roshani GH, Nazemi B, Corniani E, Nazemi E. Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products. NUCLEAR ENGINEERING AND TECHNOLOGY 2021. [DOI: 10.1016/j.net.2020.09.015] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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