Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms
<p dir="ltr">Pressure gradient (PG) in liquid-liquid flow is one of the key components to design an energy-efficient transportation system for wellbores. This study aims to develop five robust machine learning (ML) algorithms and their fusions for a wide range of flow patterns (FP) r...
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2022
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| _version_ | 1864513547774459904 |
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| author | Md Ferdous Wahid (13485799) |
| author2 | Reza Tafreshi (17269231) Zurwa Khan (17269234) Albertus Retnanto (17269237) |
| author2_role | author author author |
| author_facet | Md Ferdous Wahid (13485799) Reza Tafreshi (17269231) Zurwa Khan (17269234) Albertus Retnanto (17269237) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md Ferdous Wahid (13485799) Reza Tafreshi (17269231) Zurwa Khan (17269234) Albertus Retnanto (17269237) |
| dc.date.none.fl_str_mv | 2022-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.petrol.2021.109265 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Prediction_of_pressure_gradient_for_oil-water_flow_A_comprehensive_analysis_on_the_performance_of_machine_learning_algorithms/24420541 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Mechanical engineering Resources engineering and extractive metallurgy Information and computing sciences Machine learning Pressure gradient Horizontal wellbore Oil-water flow Machine-learning Flow pattern |
| dc.title.none.fl_str_mv | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Pressure gradient (PG) in liquid-liquid flow is one of the key components to design an energy-efficient transportation system for wellbores. This study aims to develop five robust machine learning (ML) algorithms and their fusions for a wide range of flow patterns (FP) regimes. The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs. A total of eleven hundred experimental data points for nine FPs (two stratified and seven dispersed patterns) in horizontal wellbores are used to develop the MLs. The MLs' performance is evaluated using the metrics including mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of variation of root mean squared error (CV-RMSE), and adjusted coefficient of determination. The evaluation metrics are cross-validated using a repeated train-test split strategy. Seven important predictor variables are identified using a supervised feature selection approach: oil and water velocities, FP, input diameter, oil and water density, and oil viscosity. The results show that the high PG prediction accuracy can be achieved using GP compared to other MLs except for the ML-fusions (p < 0.05). A Friedman's test and Wilcoxon Sign-Rank post hoc analysis with Bonferroni correction show that PG prediction errors using GP are significantly lower than using the ANN model (p < 0.05). The values are 18.44 % and 23.9 % for CV-RMSE, 11.6 % and 10.06 % for MAPE, and 7.5 % and 6.75 % for MdAPE, using ANN and GP, respectively. While the previous studies mostly used ANN to demonstrate the capability of MLs to predict PG over the mechanistic or correlation-based models, the present research has shown that GP is even better than ANN using a wide range of FPs and a large data set.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Petroleum Science and Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.petrol.2021.109265" target="_blank">https://dx.doi.org/10.1016/j.petrol.2021.109265</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_67e64c713807f313d746119d05d003ff |
| identifier_str_mv | 10.1016/j.petrol.2021.109265 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24420541 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithmsMd Ferdous Wahid (13485799)Reza Tafreshi (17269231)Zurwa Khan (17269234)Albertus Retnanto (17269237)EngineeringMechanical engineeringResources engineering and extractive metallurgyInformation and computing sciencesMachine learningPressure gradientHorizontal wellboreOil-water flowMachine-learningFlow pattern<p dir="ltr">Pressure gradient (PG) in liquid-liquid flow is one of the key components to design an energy-efficient transportation system for wellbores. This study aims to develop five robust machine learning (ML) algorithms and their fusions for a wide range of flow patterns (FP) regimes. The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs. A total of eleven hundred experimental data points for nine FPs (two stratified and seven dispersed patterns) in horizontal wellbores are used to develop the MLs. The MLs' performance is evaluated using the metrics including mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of variation of root mean squared error (CV-RMSE), and adjusted coefficient of determination. The evaluation metrics are cross-validated using a repeated train-test split strategy. Seven important predictor variables are identified using a supervised feature selection approach: oil and water velocities, FP, input diameter, oil and water density, and oil viscosity. The results show that the high PG prediction accuracy can be achieved using GP compared to other MLs except for the ML-fusions (p < 0.05). A Friedman's test and Wilcoxon Sign-Rank post hoc analysis with Bonferroni correction show that PG prediction errors using GP are significantly lower than using the ANN model (p < 0.05). The values are 18.44 % and 23.9 % for CV-RMSE, 11.6 % and 10.06 % for MAPE, and 7.5 % and 6.75 % for MdAPE, using ANN and GP, respectively. While the previous studies mostly used ANN to demonstrate the capability of MLs to predict PG over the mechanistic or correlation-based models, the present research has shown that GP is even better than ANN using a wide range of FPs and a large data set.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Petroleum Science and Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.petrol.2021.109265" target="_blank">https://dx.doi.org/10.1016/j.petrol.2021.109265</a></p>2022-01-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.petrol.2021.109265https://figshare.com/articles/journal_contribution/Prediction_of_pressure_gradient_for_oil-water_flow_A_comprehensive_analysis_on_the_performance_of_machine_learning_algorithms/24420541CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/244205412022-01-01T00:00:00Z |
| spellingShingle | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms Md Ferdous Wahid (13485799) Engineering Mechanical engineering Resources engineering and extractive metallurgy Information and computing sciences Machine learning Pressure gradient Horizontal wellbore Oil-water flow Machine-learning Flow pattern |
| status_str | publishedVersion |
| title | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms |
| title_full | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms |
| title_fullStr | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms |
| title_full_unstemmed | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms |
| title_short | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms |
| title_sort | Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms |
| topic | Engineering Mechanical engineering Resources engineering and extractive metallurgy Information and computing sciences Machine learning Pressure gradient Horizontal wellbore Oil-water flow Machine-learning Flow pattern |