relation: https://khub.utp.edu.my/scholars/18604/
title: Prediction of critical total drawdown in sand production from gas wells: Machine learning approach
creator: Alakbari, F.S.
creator: Mohyaldinn, M.E.
creator: Ayoub, M.A.
creator: Muhsan, A.S.
creator: Abdulkadir, S.J.
creator: Hussein, I.A.
creator: Salih, A.A.
description: 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 (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. © 2022 The Authors. The Canadian Journal of Chemical Engineering published by Wiley Periodicals LLC on behalf of Canadian Society for Chemical Engineering.
date: 2023
type: Article
type: PeerReviewed
identifier:   Alakbari, F.S. and Mohyaldinn, M.E. and Ayoub, M.A. and Muhsan, A.S. and Abdulkadir, S.J. and Hussein, I.A. and Salih, A.A.  (2023) Prediction of critical total drawdown in sand production from gas wells: Machine learning approach.  Canadian Journal of Chemical Engineering, 101 (5).  pp. 2493-2509.      
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141405710&doi=10.1002%2fcjce.24640&partnerID=40&md5=70d4642c31b89d3bf759f3012c03e5aa
relation: 10.1002/cjce.24640
identifier: 10.1002/cjce.24640