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A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells
Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075206/ https://www.ncbi.nlm.nih.gov/pubmed/33901240 http://dx.doi.org/10.1371/journal.pone.0250466 |
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author | Alakbari, Fahd Saeed Mohyaldinn, Mysara Eissa Ayoub, Mohammed Abdalla Muhsan, Ali Samer Hussein, Ibnelwaleed A. |
author_facet | Alakbari, Fahd Saeed Mohyaldinn, Mysara Eissa Ayoub, Mohammed Abdalla Muhsan, Ali Samer Hussein, Ibnelwaleed A. |
author_sort | Alakbari, Fahd Saeed |
collection | PubMed |
description | Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model’s reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD. |
format | Online Article Text |
id | pubmed-8075206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-80752062021-05-05 A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells Alakbari, Fahd Saeed Mohyaldinn, Mysara Eissa Ayoub, Mohammed Abdalla Muhsan, Ali Samer Hussein, Ibnelwaleed A. PLoS One Research Article Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model’s reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD. Public Library of Science 2021-04-26 /pmc/articles/PMC8075206/ /pubmed/33901240 http://dx.doi.org/10.1371/journal.pone.0250466 Text en © 2021 Alakbari et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Alakbari, Fahd Saeed Mohyaldinn, Mysara Eissa Ayoub, Mohammed Abdalla Muhsan, Ali Samer Hussein, Ibnelwaleed A. A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells |
title | A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells |
title_full | A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells |
title_fullStr | A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells |
title_full_unstemmed | A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells |
title_short | A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells |
title_sort | robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075206/ https://www.ncbi.nlm.nih.gov/pubmed/33901240 http://dx.doi.org/10.1371/journal.pone.0250466 |
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