Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data
| Parent link: | Journal of Petroleum Exploration and Production Vol. 11, iss. 12.— 2021.— [P. 4375-4397] |
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| Institution som forfatter: | |
| Andre forfattere: | , , , , , , |
| Summary: | Title screen One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction. Режим доступа: по договору с организацией-держателем ресурса |
| Sprog: | engelsk |
| Udgivet: |
2021
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| Fag: | |
| Online adgang: | https://doi.org/10.1007/s13202-021-01321-z |
| Format: | Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665667 |
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| 200 | 1 | |a Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data |f M. Rajabi, S. Beheshtian, Sh. Davoodi [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 330 | |a One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Journal of Petroleum Exploration and Production | ||
| 463 | |t Vol. 11, iss. 12 |v [P. 4375-4397] |d 2021 | ||
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a fracture density | |
| 610 | 1 | |a multi-hidden layer extreme learning machine | |
| 610 | 1 | |a hybrid machine learning algorithms | |
| 610 | 1 | |a multi-layer perceptron | |
| 610 | 1 | |a плотность | |
| 610 | 1 | |a трещины | |
| 610 | 1 | |a гибридное обучение | |
| 701 | 1 | |a Rajabi |b M. |g Meysam | |
| 701 | 1 | |a Beheshtian |b S. |g Saeed | |
| 701 | 1 | |a Davoodi |b Sh. |c specialist in the field of petroleum engineering |c Research Engineer of Tomsk Polytechnic University |f 1990- |g Shadfar |3 (RuTPU)RU\TPU\pers\46542 |9 22200 | |
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| 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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| 856 | 4 | |u https://doi.org/10.1007/s13202-021-01321-z | |
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