The most accurate heuristic-based algorithms for estimating the oil formation volume factor

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@Article{Mahdiani:2016:Petroleum,
  author =       "Mohammad Reza Mahdiani and Ghazal Kooti",
  title =        "The most accurate heuristic-based algorithms for
                 estimating the oil formation volume factor",
  journal =      "Petroleum",
  volume =       "2",
  number =       "1",
  pages =        "40--48",
  year =         "2016",
  ISSN =         "2405-6561",
  DOI =          "doi:10.1016/j.petlm.2015.12.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S240565611600002X",
  abstract =     "There are various types of oils in distinct
                 situations, and it is essential to discover a model for
                 estimating their oil formation volume factors which are
                 necessary for studying and simulating the reservoirs.
                 There are different correlations for estimating this,
                 but most of them have large errors (at least in some
                 points) and cannot be tuned for a specific oil. In this
                 paper, using a wide range of experimental data points,
                 an artificial neural network model (ANN) has been
                 created. In which its internal parameters (number of
                 hidden layers, number of neurons of each layer and
                 forward or backward propagation) are optimized by a
                 genetic algorithm to improve the accuracy of the model.
                 In addition, four genetic programming (GP)-based models
                 have been represented to predict the oil formation
                 volume factor In these models, the accuracy and the
                 simplicity of each equation are surveyed. As well as,
                 the effect of modifying of the internal parameters of
                 the genetic programming (by using some other values for
                 its nodes or changing the tree depth) on the created
                 model. Finally, the ANN and GP models are compared with
                 fifteen other models of the most common previously
                 introduced ones. Results show that the optimized
                 artificial neural network is the most accurate and
                 genetic programming is the most flexible model, which
                 lets the user set its accuracy and simplicity. Results
                 also recommend not adding another operator to the basic
                 operators of the genetic programming.",
  keywords =     "genetic algorithms, genetic programming, Neural
                 network, Modelling",
}

Genetic Programming entries for Mohammad Reza Mahdiani Ghazal Kooti

Citations