首页|A gated recurrent unit model to predict Poisson's ratio using deep learning

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

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Static Poisson's ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some 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 behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting vs based on a broad range of data,vs(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)(72.9-341.2 μs/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The 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 models.The 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％,respectively.The 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.

Static Poisson's ratioDeep learningGated recurrent unit(GRU)Sand controlTrend analysisGeomechanical properties

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

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Petroleum Engineering Department,Universiti Teknologi PETRONAS,32610,Bandar Seri Iskandar,Perak,Malaysia

Institute of Hydrocarbon Recovery,Universiti Teknologi PETRONAS,32610,Bandar Seri Iskandar,Perak,Malaysia

Gas Processing Center,College of Engineering,Qatar University,P.O.Box 2713,Doha,Qatar

Department of Chemical Engineering,College of Engineering,Qatar University,P.O.Box 2713,Doha,Qatar

Mechanical Engineering Department,Universiti Teknologi PETRONAS,32610,Bandar Seri Iskandar,Perak,Malaysia

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Yayasan Universiti Teknologi PETRONAS

YUTP FRG 015LC0-428

2024

10.1016/j.jrmge.2023.04.012

岩石力学与岩土工程学报(英文版)
中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD
影响因子：0.404
ISSN：1674-7755
年,卷(期)：2024.16(1)
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