Using Gene Expression Programming to estimate sonic log distributions based on the natural gamma ray and deep resistivity logs: A case study from the Anadarko Basin, Oklahoma

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@Article{Cranganu2010243,
  author =       "Constantin Cranganu and Elena Bautu",
  title =        "Using Gene Expression Programming to estimate sonic
                 log distributions based on the natural gamma ray and
                 deep resistivity logs: A case study from the Anadarko
                 Basin, Oklahoma",
  journal =      "Journal of Petroleum Science and Engineering",
  volume =       "70",
  number =       "3-4",
  pages =        "243--255",
  year =         "2010",
  ISSN =         "0920-4105",
  DOI =          "doi:10.1016/j.petrol.2009.11.017",
  URL =          "http://www.sciencedirect.com/science/article/B6VDW-4XTNG6D-7/2/f3e31340cb8a863475bff4f643de28a9",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, soft computing, sonic log,
                 Anadarko Basin, overpressured zones",
  abstract =     "In the oil and gas industry, characterisation of
                 pore-fluid pressures and rock lithology, along with
                 estimation of porosity, permeability, fluid saturation
                 and other physical properties is of crucial importance
                 for successful exploration and exploitation. Along with
                 other well logging methods, the compressional acoustic
                 (sonic) log (DT) is often used as a predictor because
                 it responds to changes in porosity or compaction and,
                 in turn, DT data are used to estimate formation
                 porosity, to map abnormal pore-fluid pressure, or to
                 perform petrophysical studies. However, despite its
                 intrinsic value, the sonic log is not routinely
                 recorded during well logging. Here we propose the use
                 of a soft computing method -- Gene Expression
                 Programming (GEP) -- to synthesise missing DT logs when
                 only common logs (such as natural gamma ray -- GR, or
                 deep resistivity -- REID) are present. The Gene
                 Expression Programming approach can be divided into
                 three steps: (1) supervised training of the model; (2)
                 confirmation and validation of the model by
                 blind-testing the results in wells containing both the
                 predictor (GR, REID) and the target (DT) values used in
                 the supervised training; and (3) applying the predicted
                 model to wells containing the predictor data and
                 obtaining the synthetic (simulated) DT log. GEP
                 methodology offers significant advantages over
                 traditional deterministic methods. It does not require
                 a precise mathematical model equation describing the
                 dependency between the predictor values and the target
                 values. Unlike linear regression techniques, GEP does
                 not over predict mean values and thereby preserves
                 original data variability. GEP also deals greatly with
                 uncertainty associated with the data, the immense size
                 of the data and the diversity of the data type. A case
                 study from the Anadarko Basin, Oklahoma, involving
                 estimating the presence of over pressured zones, is
                 presented. The results are promising and encouraging.",
}

Genetic Programming entries for Constantin Cranganu Elena Bautu

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