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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...

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Autores principales: Alakbari, Fahd Saeed, Mohyaldinn, Mysara Eissa, Ayoub, Mohammed Abdalla, Muhsan, Ali Samer, Hussein, Ibnelwaleed A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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.
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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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