Computational intelligence for deepwater reservoir depositional environments interpretation

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Tina Yu and Dave Wilkinson and Julian Clark and 
                 Morgan Sullivan",
  title =        "Computational intelligence for deepwater reservoir
                 depositional environments interpretation",
  journal =      "Journal of Natural Gas Science and Engineering",
  volume =       "3",
  number =       "6",
  pages =        "716--728",
  year =         "2011",
  note =         "Artificial Intelligence and Data Mining",
  ISSN =         "1875-5100",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1016/j.jngse.2011.07.014",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Deep water
                 reservoir, Stratigraphic interpretation, Depositional
                 environment, Gamma ray interpretation, Computational
                 intelligence, Fuzzy logic, Well log, Co-evolution, Time
                 series, Segmentation, Finite state transducer,
                 Classification rules",
  abstract =     "Predicting oil recovery efficiency of a deepwater
                 reservoir is a challenging task. One approach to
                 characterise a deepwater reservoir and to predict its
                 producibility is by analysing its depositional
                 information. This research proposes a deposition-based
                 stratigraphic interpretation framework for deep water
                 reservoir characterisation. In this framework, one
                 critical task is the identification and labelling of
                 the stratigraphic components in the reservoir,
                 according to their depositional environments. This
                 interpretation process is labour intensive and can
                 produce different results depending on the
                 stratigrapher who performs the analysis. To relieve
                 stratigrapher's workload and to produce more consistent
                 results, we have developed a novel methodology to
                 automate this process using various computational
                 intelligence techniques. Using a well log data set, we
                 demonstrate that the developed methodology and the
                 designed workflow can produce finite state transducer
                 models that interpret deepwater reservoir depositional
                 environments adequately.",

Genetic Programming entries for Tina Yu Dave Wilkinson Julian Clark Morgan Sullivan