Predicting oil flow rate through orifice plate with robust machine learning algorithms; Flow Measurement and Instrumentation; Vol. 81
| Parent link: | Flow Measurement and Instrumentation Vol. 81.— 2021.— [102047, 9 p.] |
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| Autor Corporativo: | |
| Otros Autores: | , , , , , , , , |
| Sumario: | Title screen Measuring fluid flow rate passing through pipelines is a basic strategy for developing the infrastructure of fluid-dependent industries. It is a challenging issue for trade, transportation, and reservoir management purposes. Predicting the flow rate of fluid is also regarded as one of the crucial steps for the development of oil fields. In this study, a novel deep machine learning model, convolutional neural network (CNN), was developed to predict oil flow rate through orifice plate (Qo) from seven input variables, including fluid temperature (Tf), upstream pressure (Pu), root differential pressure (√ΔP), percentage of base sediment and water (BS&W%), oil specific gravity (SG), kinematic viscosity (ν), and beta ratio (β, the ratio of pipe diameter to orifice diameter). Due to the absence of accurate and credible methods for determining Qo, deep learning can be a useful alternative to traditional machine learning methods. Justifying the promising performance of the developed CNN model over conventional machine learning models, three different machine learning algorithms, including radial basis function (RBF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), were also developed and their prediction performance was compared with that of the CNN model. A sensitivity analysis was also performed on the influence degree of each input variable on the output variable (Qo). The study outcomes indicate that the CNN model provided the highest Qo prediction accuracy among all the four models developed by presenting a root mean squared error (RMSE) of 0.0341 m3/s and a coefficient of determination (R2) of 0.9999, when applied to the dataset of 3303 data records compiled from oil fields around Iran. The Spearman correlation coefficient analysis results display that √ΔP, Pu, and Tf were the most influential variables on the oil flow rate in respect of the large dataset evaluated. Режим доступа: по договору с организацией-держателем ресурса |
| Lenguaje: | inglés |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | https://doi.org/10.1016/j.flowmeasinst.2021.102047 |
| Formato: | Electrónico Capítulo de libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665550 |
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| 200 | 1 | |a Predicting oil flow rate through orifice plate with robust machine learning algorithms |f A. R. Behesht Abad, P. S. Tehrani, M. Naveshki [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 330 | |a Measuring fluid flow rate passing through pipelines is a basic strategy for developing the infrastructure of fluid-dependent industries. It is a challenging issue for trade, transportation, and reservoir management purposes. Predicting the flow rate of fluid is also regarded as one of the crucial steps for the development of oil fields. In this study, a novel deep machine learning model, convolutional neural network (CNN), was developed to predict oil flow rate through orifice plate (Qo) from seven input variables, including fluid temperature (Tf), upstream pressure (Pu), root differential pressure (√ΔP), percentage of base sediment and water (BS&W%), oil specific gravity (SG), kinematic viscosity (ν), and beta ratio (β, the ratio of pipe diameter to orifice diameter). Due to the absence of accurate and credible methods for determining Qo, deep learning can be a useful alternative to traditional machine learning methods. Justifying the promising performance of the developed CNN model over conventional machine learning models, three different machine learning algorithms, including radial basis function (RBF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), were also developed and their prediction performance was compared with that of the CNN model. A sensitivity analysis was also performed on the influence degree of each input variable on the output variable (Qo). The study outcomes indicate that the CNN model provided the highest Qo prediction accuracy among all the four models developed by presenting a root mean squared error (RMSE) of 0.0341 m3/s and a coefficient of determination (R2) of 0.9999, when applied to the dataset of 3303 data records compiled from oil fields around Iran. The Spearman correlation coefficient analysis results display that √ΔP, Pu, and Tf were the most influential variables on the oil flow rate in respect of the large dataset evaluated. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Flow Measurement and Instrumentation | ||
| 463 | |t Vol. 81 |v [102047, 9 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a oil flow rate measurement | |
| 610 | 1 | |a orifice flow meter | |
| 610 | 1 | |a ultrasonic flow meter | |
| 610 | 1 | |a deep learning | |
| 610 | 1 | |a machine learning algorithm | |
| 610 | 1 | |a convolutional neural network | |
| 610 | 1 | |a расходомеры | |
| 610 | 1 | |a машинное обучение | |
| 610 | 1 | |a сверточные нейронные сети | |
| 701 | 1 | |a Behesht Abad |b A. R. |g Abouzar Rajabi | |
| 701 | 1 | |a Tehrani |b P. S. |g Pezhman Soltani | |
| 701 | 1 | |a Naveshki |b M. |g Mohammad | |
| 701 | 1 | |a Ghorbani |b H. |g Hamzeh | |
| 701 | 1 | |a Mohamadian |b N. |g Nima | |
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| 701 | 1 | |a Saberi |b H. |g Hossein | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа природных ресурсов |b Отделение нефтегазового дела |3 (RuTPU)RU\TPU\col\23546 |
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