Multilayer perceptron with particle swarm optimization for well log data inversion

Kou-Yuan Huang*, Kai Ju Chen, Ming Che Huang, Liang Chi Shen

*Corresponding author for this work

    Research output: Contribution to conferencePaperpeer-review

    1 Scopus citations

    Abstract

    A nonlinear mapping exists between the measured apparent conductivity (Ca) and the true formation conductivity (Ct). We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm optimization with mutation (MPSO) algorithm to adjust the weights in MLP. In the supervised training step, the input of the network is the measured Ca and the desired output is the Ct. MLP with optimal size 10-9-10 is chosen as the model. We have experiments in simulation and real data application. In simulation, there are 31 sets of simulated well log data, where 25 sets are used for training, and 6 sets are used for testing. After training the MLP network, input Ca, then Ct' can be inverted in testing process. Compared with radial basis function (RBF) networks and particle swarm optimization (PSO) method, the error of MPSO is the smallest. Also we apply it to the inversion of real field well log data. The result is acceptable. It shows that the proposed MPSO algorithm in MLP weight adjustments can perform the well log data inversion.

    Original languageEnglish
    Pages6103-6106
    Number of pages4
    DOIs
    StatePublished - 1 Dec 2012
    Event2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany
    Duration: 22 Jul 201227 Jul 2012

    Conference

    Conference2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
    Country/TerritoryGermany
    CityMunich
    Period22/07/1227/07/12

    Keywords

    • apparent conductivity (C)
    • multilayer perceptron (MLP)
    • particle swarm optimization with mutation (MPSO)
    • true formation conductivity (C)

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