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Prediction of permeability from well logs using a new hybrid machine learning algorithm

Petroleum 2023年第1期9卷 108-123页

作者： Morteza Matinkia Romina Hashami Mohammad Mehrad Mohammad Reza Hajsaeedi Arian Velayati Department of Petroleum Engineering Omidiyeh BranchIslamic Azad UniversityOmidiyehIran Department of Applied Mathematics Faculty of Mathematics and Computer SciencesAmirkabir University of TechnologyTehranIran Faculty of Mining Petroleum and Geophysics EngineeringShahrood University of TechnologyShahroodIran Department of Chemical and Materials Engineering University of AlbertaEdmontonCanada

Permeability is a measure of fluid transmissibility in the rock and is a crucial concept in the evaluation of formations and the production of hydrocarbon from the *** techniques such as intelligent methods have been introduced to estimate the permeability from other petrophysical *** efficiency and convergence issues associated with artificial neural networks have motivated researchers to use hybrid techniques for the optimization of the networks,where the artificial neural network is combined with heuristic *** research combines social ski-driver(SSD)algorithm with the multilayer perception(MLP)neural network and presents a new hybrid algorithm to predict the value of rock *** performance of this novel technique is compared with two previously used hybrid methods(genetic algorithm-MLP and particle swarm optimization-MLP)to examine the effectiveness of these hybrid methods in predicting the permeability of the *** results indicate that the hybrid models can predict rock permeability with excellent ***-SSD method yields the highest coefficient of determination(0.9928)among all other methods in predicting the permeability values of the test data set,followed by MLP-PSO and MLP-GA,***,the MLP-GA converged faster than the other two methods and is computationally less expensive.

关键词： Permeability Artificial neural network Multilayer perceptron social ski driver algorithm

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