Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ayoub Mohammed, Mohammed Abdalla, Alakbari, Fahd Saeed, Nathan, Clarence Prebla, Mohyaldinn, Mysara Eissa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
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
_version_ 1784728497883709440
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
work_keys_str_mv AT ayoubmohammedmohammedabdalla determinationofthegasoilratiobelowthebubblepointpressureusingtheadaptiveneurofuzzyinferencesystemanfis
AT alakbarifahdsaeed determinationofthegasoilratiobelowthebubblepointpressureusingtheadaptiveneurofuzzyinferencesystemanfis
AT nathanclarenceprebla determinationofthegasoilratiobelowthebubblepointpressureusingtheadaptiveneurofuzzyinferencesystemanfis
AT mohyaldinnmysaraeissa determinationofthegasoilratiobelowthebubblepointpressureusingtheadaptiveneurofuzzyinferencesystemanfis