Prediction of critical total drawdown in sand production from gas wells: Machine learning approach
<p></p><div> <p>Sand production is a critical issue in petroleum wells. The critical total drawdown (CTD) is an essential indicator of the onset of sand production. Although some models are available for CTD prediction, most of them are proven to lack accuracy or use commerci...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , |
| منشور في: |
2023
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513565650583552 |
|---|---|
| author | Fahd Saeed Alakbari (10701871) |
| author2 | Mysara Eissa Mohyaldinn (10701874) Mohammed Abdalla Ayoub (10701877) Ali Samer Muhsan (10701880) Said Jadid Abdulkadir (14778133) Ibnelwaleed A. Hussein (5535953) Abdullah Abduljabbar Salih (14778136) |
| author2_role | author author author author author author |
| author_facet | Fahd Saeed Alakbari (10701871) Mysara Eissa Mohyaldinn (10701874) Mohammed Abdalla Ayoub (10701877) Ali Samer Muhsan (10701880) Said Jadid Abdulkadir (14778133) Ibnelwaleed A. Hussein (5535953) Abdullah Abduljabbar Salih (14778136) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fahd Saeed Alakbari (10701871) Mysara Eissa Mohyaldinn (10701874) Mohammed Abdalla Ayoub (10701877) Ali Samer Muhsan (10701880) Said Jadid Abdulkadir (14778133) Ibnelwaleed A. Hussein (5535953) Abdullah Abduljabbar Salih (14778136) |
| dc.date.none.fl_str_mv | 2023-03-16T06:21:55Z |
| dc.identifier.none.fl_str_mv | 10.1002/cjce.24640 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Prediction_of_critical_total_drawdown_in_sand_production_from_gas_wells_Machine_learning_approach/22257979 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Chemical engineering General Chemical Engineering |
| dc.title.none.fl_str_mv | Prediction of critical total drawdown in sand production from gas wells: Machine learning approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p></p><div> <p>Sand production is a critical issue in petroleum wells. The critical total drawdown (CTD) is an essential indicator of the onset of sand production. Although some models are available for CTD prediction, most of them are proven to lack accuracy or use commercial software. Furthermore, the previous correlations have not studied the trend analysis to verify the correct relationships between the parameters. Therefore, this study aims to build accurate and robust models for predicting CTD using response surface methodology (RSM) and support vector machine (SVM). The RSM is utilized to obtain the equation without using any software. The SVM model is an alternative method to predict the CTD with higher accuracy. This study used 23 datasets to develop the proposed models. The CTD is a strong function of the total vertical depth, cohesive strength, effective overburden vertical stress, and transit time with correlation coefficients (<i>R</i>) of 0.968, 0.963, 0.918, and −0.813. Different statistical methods, that is, analysis of variance (ANOVA), <i>F</i>-statistics test, fit statistics, and diagnostics plots, have shown that the RSM correlation has high accuracy and is more robust than correlations reported in the literature. Moreover, trend analysis has proven that the proposed models ideally follow the correct trend. The RSM correlation decreased the average absolute percent relative error (AAPRE) by 12.7% compared to all published correlations' AAPRE of 22.6%–30.4%. The SVM model has shown the lowest AAPRE of 6.1%, with the highest <i>R</i> of 0.995. The effects of all independent variables on the CTD are displayed in three-dimensional plots and showed significant interactions.</p> </div><p></p><h2>Other Information</h2> <p> Published in: The Canadian Journal of Chemical Engineering<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1002/cjce.24640" target="_blank">http://dx.doi.org/10.1002/cjce.24640</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1c897ec7baa0e2155e77d3b1b2440ec3 |
| identifier_str_mv | 10.1002/cjce.24640 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/22257979 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Prediction of critical total drawdown in sand production from gas wells: Machine learning approachFahd Saeed Alakbari (10701871)Mysara Eissa Mohyaldinn (10701874)Mohammed Abdalla Ayoub (10701877)Ali Samer Muhsan (10701880)Said Jadid Abdulkadir (14778133)Ibnelwaleed A. Hussein (5535953)Abdullah Abduljabbar Salih (14778136)EngineeringChemical engineeringGeneral Chemical Engineering<p></p><div> <p>Sand production is a critical issue in petroleum wells. The critical total drawdown (CTD) is an essential indicator of the onset of sand production. Although some models are available for CTD prediction, most of them are proven to lack accuracy or use commercial software. Furthermore, the previous correlations have not studied the trend analysis to verify the correct relationships between the parameters. Therefore, this study aims to build accurate and robust models for predicting CTD using response surface methodology (RSM) and support vector machine (SVM). The RSM is utilized to obtain the equation without using any software. The SVM model is an alternative method to predict the CTD with higher accuracy. This study used 23 datasets to develop the proposed models. The CTD is a strong function of the total vertical depth, cohesive strength, effective overburden vertical stress, and transit time with correlation coefficients (<i>R</i>) of 0.968, 0.963, 0.918, and −0.813. Different statistical methods, that is, analysis of variance (ANOVA), <i>F</i>-statistics test, fit statistics, and diagnostics plots, have shown that the RSM correlation has high accuracy and is more robust than correlations reported in the literature. Moreover, trend analysis has proven that the proposed models ideally follow the correct trend. The RSM correlation decreased the average absolute percent relative error (AAPRE) by 12.7% compared to all published correlations' AAPRE of 22.6%–30.4%. The SVM model has shown the lowest AAPRE of 6.1%, with the highest <i>R</i> of 0.995. The effects of all independent variables on the CTD are displayed in three-dimensional plots and showed significant interactions.</p> </div><p></p><h2>Other Information</h2> <p> Published in: The Canadian Journal of Chemical Engineering<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1002/cjce.24640" target="_blank">http://dx.doi.org/10.1002/cjce.24640</a></p>2023-03-16T06:21:55ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/cjce.24640https://figshare.com/articles/journal_contribution/Prediction_of_critical_total_drawdown_in_sand_production_from_gas_wells_Machine_learning_approach/22257979CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/222579792023-03-16T06:21:55Z |
| spellingShingle | Prediction of critical total drawdown in sand production from gas wells: Machine learning approach Fahd Saeed Alakbari (10701871) Engineering Chemical engineering General Chemical Engineering |
| status_str | publishedVersion |
| title | Prediction of critical total drawdown in sand production from gas wells: Machine learning approach |
| title_full | Prediction of critical total drawdown in sand production from gas wells: Machine learning approach |
| title_fullStr | Prediction of critical total drawdown in sand production from gas wells: Machine learning approach |
| title_full_unstemmed | Prediction of critical total drawdown in sand production from gas wells: Machine learning approach |
| title_short | Prediction of critical total drawdown in sand production from gas wells: Machine learning approach |
| title_sort | Prediction of critical total drawdown in sand production from gas wells: Machine learning approach |
| topic | Engineering Chemical engineering General Chemical Engineering |