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    Lithological facies identification in Iranian largest gas field: A comparative study of neural network methods

    , Article Journal of the Geological Society of India ; Vol. 84, issue. 3 , Sep , 2014 , p. 326-334 ; ISSN: 00167622 Kakouei, A ; Masihi, M ; Sola, B. S ; Biniaz, E ; Sharif University of Technology
    Abstract
    Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),... 

    Lithological facies identification in Iranian largest gas field: A comparative study of neural network methods

    , Article Journal of the Geological Society of India ; Vol. 84, issue. 3 , September , 2014 , PP. 326-334 ; ISSN: 00167622 Kakouei, A ; Masihi, M ; Sola, B. S ; Biniaz, E ; Sharif University of Technology
    Abstract
    Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),... 

    Joint estimation of facies boundaries and petrophysical properties in multi-facies channelized reservoirs through ensemble-based Kalman filter and level set parametrization

    , Article Journal of Petroleum Science and Engineering ; Volume 167 , 2018 , Pages 752-773 ; 09204105 (ISSN) Jahanbakhshi, S ; Pishvaie, M. R ; Boozarjomehry, R. B ; Sharif University of Technology
    Elsevier B.V  2018
    Abstract
    Ensemble-based assimilation methods are the most promising tools for dynamic characterization of reservoir models. However, because of inherent assumption of Gaussianity, these methods are not directly applicable to channelized reservoirs wherein the distribution of petrophysical properties is multimodal. Transformation of facies field to level set functions have been proposed to alleviate the problem of multimodality. Level set representation ensures that the estimated fields are facies realizations as well as no modification of the assimilation method is required. Moreover, due to the complexity of the history matching problem in the channelized reservoirs, most researchers conventionally...