A hybrid stochastic-gradient optimization to estimating total organic carbon from petrophysical data: A case study from the Ahwaz oilfield, SW Iran

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@Article{Tabatabaei:2015:JPSE,
  author =       "Seyed Mohammad Ehsan Tabatabaei and 
                 Ali Kadkhodaie-Ilkhchi and Ziba Hosseini and 
                 Asghar Asghari Moghaddam",
  title =        "A hybrid stochastic-gradient optimization to
                 estimating total organic carbon from petrophysical
                 data: A case study from the {Ahwaz} oilfield, {SW
                 Iran}",
  journal =      "Journal of Petroleum Science and Engineering",
  year =         "2015",
  volume =       "127",
  pages =        "35--43",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, total organic
                 carbon (TOC), petrophysical logs, ACOR-BP, GA-BP, the
                 Ahwaz oilfield",
  ISSN =         "0920-4105",
  DOI =          "doi:10.1016/j.petrol.2015.01.028",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0920410515000297",
  abstract =     "One of the most important geochemical data in
                 petroleum exploration is total organic carbon (TOC)
                 which is used to evaluate the hydrocarbon generation
                 potential of source rocks. To measure this parameter,
                 expensive and time-consuming geochemical experiments
                 are carried out on few cutting or core samples. In this
                 study, stochastic optimisation algorithms (ant colony
                 and genetic programming) were hybridised with gradient
                 optimisation in a back propagation neural network
                 structure to estimate TOC from petrophysical logs. The
                 methodology is illustrated by using a case study from
                 four wells of the Ahwaz oilfield. The results show that
                 the hybrid ant colony-back propagation neural network
                 model (ACOR-BP) provides better results compared to the
                 other intelligent models used. MSE and R2 of the
                 ACOR-BP model in testing samples are 0.0051 and 0.952,
                 respectively. This level of accuracy along with the
                 fast speed of the algorithm is highly desirable for the
                 estimation of the TOC parameter. The findings of this
                 research demonstrate that employing ant colony
                 optimization to initialise weights and biases of neural
                 networks minimises or avoids the risk of getting stuck
                 in local minima. The methodology introduced in this
                 study has a good performance and can be used to
                 synthesise geochemical logs for the other wells of the
                 Ahwaz oilfield.",
}

Genetic Programming entries for Seyed Mohammad Ehsan Tabatabaei Ali Kadkhodaie-Ilkhchi Ziba Hosseini Asghar Asghari Moghaddam

Citations