Modeling oil production based on symbolic regression

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@Article{Yang:2015a:EP,
  author =       "Guangfei Yang and Xianneng Li and Jianliang Wang and 
                 Lian Lian and Tieju Ma",
  title =        "Modeling oil production based on symbolic regression",
  journal =      "Energy Policy",
  year =         "2015",
  volume =       "82",
  number =       "Supplement C",
  pages =        "48--61",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Oil
                 production, Hubbert theory",
  ISSN =         "0301-4215",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0301421515000798",
  DOI =          "doi:10.1016/j.enpol.2015.02.016",
  abstract =     "Numerous models have been proposed to forecast the
                 future trends of oil production and almost all of them
                 are based on some predefined assumptions with various
                 uncertainties. In this study, we propose a novel
                 data-driven approach that uses symbolic regression to
                 model oil production. We validate our approach on both
                 synthetic and real data, and the results prove that
                 symbolic regression could effectively identify the true
                 models beneath the oil production data and also make
                 reliable predictions. Symbolic regression indicates
                 that world oil production will peak in 2021, which
                 broadly agrees with other techniques used by
                 researchers. Our results also show that the rate of
                 decline after the peak is almost half the rate of
                 increase before the peak, and it takes nearly 12 years
                 to drop 4% from the peak. These predictions are more
                 optimistic than those in several other reports, and the
                 smoother decline will provide the world, especially the
                 developing countries, with more time to orchestrate
                 mitigation plans.",
}

Genetic Programming entries for Guangfei Yang Xianneng Li Jianliang Wang Lian Lian Tieju Ma

Citations