An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique
<p dir="ltr">Sand production causes many problems in the petroleum industry. The sand production is predicted to control it in the early stages. Therefore, accurate prediction of sand production has been considered substantial in achieving successful sand control. Critical total draw...
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2024
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| author | Fahd Saeed Alakbari (10701871) |
| author2 | Syed Mohammad Mahmood (9329464) Mysara Eissa Mohyaldinn (10701874) Mohammed Abdalla Ayoub (10701877) Ibnelwaleed A. Hussein (5535953) Ali Samer Muhsan (10701880) Abdullah Abduljabbar Salih (14778136) Azza Hashim Abbas (19176655) |
| author2_role | author author author author author author author |
| author_facet | Fahd Saeed Alakbari (10701871) Syed Mohammad Mahmood (9329464) Mysara Eissa Mohyaldinn (10701874) Mohammed Abdalla Ayoub (10701877) Ibnelwaleed A. Hussein (5535953) Ali Samer Muhsan (10701880) Abdullah Abduljabbar Salih (14778136) Azza Hashim Abbas (19176655) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fahd Saeed Alakbari (10701871) Syed Mohammad Mahmood (9329464) Mysara Eissa Mohyaldinn (10701874) Mohammed Abdalla Ayoub (10701877) Ibnelwaleed A. Hussein (5535953) Ali Samer Muhsan (10701880) Abdullah Abduljabbar Salih (14778136) Azza Hashim Abbas (19176655) |
| dc.date.none.fl_str_mv | 2024-09-23T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s13369-024-09556-8 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_Accurate_Critical_Total_Drawdown_Prediction_Model_for_Sand_Production_Adaptive_Neuro-fuzzy_Inference_System_ANFIS_Technique/30024160 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Sand control Machine learning Adaptive neuro-fuzzy inference system technique ANFIS Artificial intelligence Critical total drawdown |
| dc.title.none.fl_str_mv | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Sand production causes many problems in the petroleum industry. The sand production is predicted to control it in the early stages. Therefore, accurate prediction of sand production has been considered substantial in achieving successful sand control. Critical total drawdown (CTD) can indicate the sand production. The main drawback of the previous studies in predicting CTD is their lack of accuracy. Thus, this study aims to develop an accurate CTD estimation prediction model employing a trend analysis and adaptive neuro-fuzzy inference system (ANFIS). The method is chosen because of its higher performance; the model is built based on 23 published datasets from the Adriatic Sea. The developed ANFIS model is evaluated using various methods, namely, trend analyses. Trend analyses are conducted to show the effects of the features on the CTD to present the physical behavior. The model’s performance was also evaluated using statistical error analyses. In addition, the ANFIS and previously published models were assessed. The trend analyses show the correct relationship between all features and the CTD. In addition, the trend analyses for the previous models are discussed. The results show that the proposed ANFIS method outperforms published methods with an R of 0.9984 and an absolute average percentage relative error (AAPRE) of 4.293%.</p><h2>Other Information</h2><p dir="ltr">Published in: Arabian Journal for Science and Engineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s13369-024-09556-8" target="_blank">https://dx.doi.org/10.1007/s13369-024-09556-8</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bb312d8ed64ddcdc8bbead6c95947b17 |
| identifier_str_mv | 10.1007/s13369-024-09556-8 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30024160 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) TechniqueFahd Saeed Alakbari (10701871)Syed Mohammad Mahmood (9329464)Mysara Eissa Mohyaldinn (10701874)Mohammed Abdalla Ayoub (10701877)Ibnelwaleed A. Hussein (5535953)Ali Samer Muhsan (10701880)Abdullah Abduljabbar Salih (14778136)Azza Hashim Abbas (19176655)EngineeringResources engineering and extractive metallurgyInformation and computing sciencesArtificial intelligenceMachine learningSand controlMachine learningAdaptive neuro-fuzzy inference system techniqueANFISArtificial intelligenceCritical total drawdown<p dir="ltr">Sand production causes many problems in the petroleum industry. The sand production is predicted to control it in the early stages. Therefore, accurate prediction of sand production has been considered substantial in achieving successful sand control. Critical total drawdown (CTD) can indicate the sand production. The main drawback of the previous studies in predicting CTD is their lack of accuracy. Thus, this study aims to develop an accurate CTD estimation prediction model employing a trend analysis and adaptive neuro-fuzzy inference system (ANFIS). The method is chosen because of its higher performance; the model is built based on 23 published datasets from the Adriatic Sea. The developed ANFIS model is evaluated using various methods, namely, trend analyses. Trend analyses are conducted to show the effects of the features on the CTD to present the physical behavior. The model’s performance was also evaluated using statistical error analyses. In addition, the ANFIS and previously published models were assessed. The trend analyses show the correct relationship between all features and the CTD. In addition, the trend analyses for the previous models are discussed. The results show that the proposed ANFIS method outperforms published methods with an R of 0.9984 and an absolute average percentage relative error (AAPRE) of 4.293%.</p><h2>Other Information</h2><p dir="ltr">Published in: Arabian Journal for Science and Engineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s13369-024-09556-8" target="_blank">https://dx.doi.org/10.1007/s13369-024-09556-8</a></p>2024-09-23T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s13369-024-09556-8https://figshare.com/articles/journal_contribution/An_Accurate_Critical_Total_Drawdown_Prediction_Model_for_Sand_Production_Adaptive_Neuro-fuzzy_Inference_System_ANFIS_Technique/30024160CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300241602024-09-23T09:00:00Z |
| spellingShingle | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique Fahd Saeed Alakbari (10701871) Engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Sand control Machine learning Adaptive neuro-fuzzy inference system technique ANFIS Artificial intelligence Critical total drawdown |
| status_str | publishedVersion |
| title | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique |
| title_full | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique |
| title_fullStr | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique |
| title_full_unstemmed | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique |
| title_short | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique |
| title_sort | An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique |
| topic | Engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Sand control Machine learning Adaptive neuro-fuzzy inference system technique ANFIS Artificial intelligence Critical total drawdown |