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Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)
[Image: see text] Determining the solution gas–oil ratio (R(s)) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas–oil ratio under the bubble po...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202275/ https://www.ncbi.nlm.nih.gov/pubmed/35721985 http://dx.doi.org/10.1021/acsomega.2c01496 |
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author | Ayoub Mohammed, Mohammed Abdalla Alakbari, Fahd Saeed Nathan, Clarence Prebla Mohyaldinn, Mysara Eissa |
author_facet | Ayoub Mohammed, Mohammed Abdalla Alakbari, Fahd Saeed Nathan, Clarence Prebla Mohyaldinn, Mysara Eissa |
author_sort | Ayoub Mohammed, Mohammed Abdalla |
collection | PubMed |
description | [Image: see text] Determining the solution gas–oil ratio (R(s)) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas–oil ratio under the bubble point. However, they still may prove unreliable due to the applied assumptions and their specification to operate only under a particular range of data. In this study, the neuro-fuzzy, i.e., the adaptive neuro-fuzzy inference system (ANFIS) approach, is utilized to develop an accurate and dependable model for determining the R(s) below the bubble point pressure. A total of 376 pressure–volume–temperature datasets from Sudanese oil fields were used to establish the proposed ANFIS model. The trend analysis was applied to affirm the proper relationships between the inputs and outputs. Furthermore, using different statistical error analyses, the developed model was benchmarked against widely used empirical methods to evaluate the proposed method’s performance in predicting the R(s) at pressures below the bubble point. The proposed ANFIS model performs with an average absolute percent relative error of 10.60% and a correlation coefficient of 99.04%, surpassing the previously studied correlations. |
format | Online Article Text |
id | pubmed-9202275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-92022752022-06-17 Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Ayoub Mohammed, Mohammed Abdalla Alakbari, Fahd Saeed Nathan, Clarence Prebla Mohyaldinn, Mysara Eissa ACS Omega [Image: see text] Determining the solution gas–oil ratio (R(s)) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas–oil ratio under the bubble point. However, they still may prove unreliable due to the applied assumptions and their specification to operate only under a particular range of data. In this study, the neuro-fuzzy, i.e., the adaptive neuro-fuzzy inference system (ANFIS) approach, is utilized to develop an accurate and dependable model for determining the R(s) below the bubble point pressure. A total of 376 pressure–volume–temperature datasets from Sudanese oil fields were used to establish the proposed ANFIS model. The trend analysis was applied to affirm the proper relationships between the inputs and outputs. Furthermore, using different statistical error analyses, the developed model was benchmarked against widely used empirical methods to evaluate the proposed method’s performance in predicting the R(s) at pressures below the bubble point. The proposed ANFIS model performs with an average absolute percent relative error of 10.60% and a correlation coefficient of 99.04%, surpassing the previously studied correlations. American Chemical Society 2022-05-31 /pmc/articles/PMC9202275/ /pubmed/35721985 http://dx.doi.org/10.1021/acsomega.2c01496 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Ayoub Mohammed, Mohammed Abdalla Alakbari, Fahd Saeed Nathan, Clarence Prebla Mohyaldinn, Mysara Eissa Determination of the Gas–Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS) |
title | Determination of the Gas–Oil Ratio below the
Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System
(ANFIS) |
title_full | Determination of the Gas–Oil Ratio below the
Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System
(ANFIS) |
title_fullStr | Determination of the Gas–Oil Ratio below the
Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System
(ANFIS) |
title_full_unstemmed | Determination of the Gas–Oil Ratio below the
Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System
(ANFIS) |
title_short | Determination of the Gas–Oil Ratio below the
Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System
(ANFIS) |
title_sort | determination of the gas–oil ratio below the
bubble point pressure using the adaptive neuro-fuzzy inference system
(anfis) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202275/ https://www.ncbi.nlm.nih.gov/pubmed/35721985 http://dx.doi.org/10.1021/acsomega.2c01496 |
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