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AuthorAlakbari, Fahd Saeed
AuthorMohyaldinn, Mysara Eissa
AuthorAyoub, Mohammed Abdalla
AuthorMuhsan, Ali Samer
AuthorAbdulkadir, Said Jadid
AuthorHussein, Ibnelwaleed A.
AuthorSalih, Abdullah Abduljabbar
Available date2023-07-12T07:28:15Z
Publication Date2023
Publication NameCanadian Journal of Chemical Engineering
ResourceScopus
URIhttp://dx.doi.org/10.1002/cjce.24640
URIhttp://hdl.handle.net/10576/45387
AbstractSand 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 (R) of 0.968, 0.963, 0.918, and −0.813. Different statistical methods, that is, analysis of variance (ANOVA), F-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 R of 0.995. The effects of all independent variables on the CTD are displayed in three-dimensional plots and showed significant interactions.
SponsorThe authors would like to thank the Yayasan Universiti Teknologi PETRONAS (YUTP FRG Grant No: 015LC0-428) at Universiti Teknologi PETRONAS financial support. Open access funding is provided by the Qatar National Library.
Languageen
PublisherJohn Wiley and Sons Inc
Subjectcritical total drawdown
machine learning
sand control
sand management
TitlePrediction of critical total drawdown in sand production from gas wells: Machine learning approach
TypeArticle
Pagination2493-2509
Issue Number5
Volume Number101


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