A gated recurrent unit model to predict Poisson’s ratio using deep learning

作     者：Fahd Saeed Alakbari Mysara Eissa Mohyaldinn Mohammed Abdalla Ayoub Ibnelwaleed A.Hussein Ali Samer Muhsan Syahrir Ridha Abdullah Abduljabbar Salih Fahd Saeed Alakbari;Mysara Eissa Mohyaldinn;Mohammed Abdalla Ayoub;Ibnelwaleed A.Hussein;Ali Samer Muhsan;Syahrir Ridha;Abdullah Abduljabbar Salih

作者机构：Petroleum Engineering DepartmentUniversiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia Institute of Hydrocarbon RecoveryUniversiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia Gas Processing CenterCollege of EngineeringQatar UniversityP.O.Box 2713DohaQatar Department of Chemical EngineeringCollege of EngineeringQatar UniversityP.O.Box 2713DohaQatar Mechanical Engineering DepartmentUniversiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia 

出 版 物：《Journal of Rock Mechanics and Geotechnical Engineering》 (岩石力学与岩土工程学报（英文版）)

年 卷 期：2024年第16卷第1期

页      面：123-135页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080104[工学-工程力学] 0815[工学-水利工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

基　　金：The authors thank the Yayasan Universiti Teknologi PETRONAS(YUTP FRG Grant No.015LC0-428)at Universiti Teknologi PETRO-NAS for supporting this study 

主　　题：Static Poisson’s ratio Deep learning Gated recurrent unit(GRU) Sand control Trend analysis Geomechanical properties 

摘      要：Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand *** models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical *** this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error *** GRU model showed the proper trends,and the model data ranges were wider than previous *** GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other *** GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,*** group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.