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

Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 123-135页

作者： Fahd Saeed Alakbari Mysara Eissa Mohyaldinn Mohammed Abdalla Ayoub Ibnelwaleed A.Hussein Ali Samer Muhsan Syahrir Ridha abdullah abduljabbar salih Petroleum Engineering Department Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia Institute of Hydrocarbon Recovery Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia Gas Processing Center College of EngineeringQatar UniversityP.O.Box 2713DohaQatar Department of Chemical Engineering College of EngineeringQatar UniversityP.O.Box 2713DohaQatar Mechanical Engineering Department Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia

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.

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

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