Permeability Estimation Using a Hybrid Genetic Programming and Fuzzy/Neural Inference Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Xie:2005:ATCE,
  author =       "Deyi Xie and David Alan Wilkinson and Tina Yu",
  title =        "Permeability Estimation Using a Hybrid Genetic
                 Programming and Fuzzy/Neural Inference Approach",
  booktitle =    "SPE Annual Technical Conference and Exhibition (ATCE
                 2005)",
  year =         "2005",
  volume =       "1",
  pages =        "176--182?",
  address =      "Dallas, Texas, USA",
  month =        "9-12 " # oct,
  publisher =    "Society of Petroleum Engineers",
  keywords =     "genetic algorithms, genetic programming, 4.1.2
                 Separation and Treating, 5.8.7 Carbonate Reservoir, 5.1
                 Reservoir Characterisation, 5.1.5 Geologic Modelling,
                 4.2 Pipelines, Flowlines and Risers, 6.1.5 Human
                 Resources, Competence and Training, 1.6.9 Coring,
                 Fishing, 1.2.3 Rock properties, 2.4.3 Sand/Solids
                 Control, 4.1.5 Processing Equipment, 5.6.1 Open
                 hole/cased hole log analysis",
  isbn13 =       "978-1-55563-150-5",
  URL =          "https://www.onepetro.org/conference-paper/SPE-95167-MS?sort=&start=0&q=Permeability+Estimation+Using+a+Hybrid+Genetic+Programming+and+Fuzzy%2FNeural+Inference+Approach&from_year=&peer_reviewed=&published_between=&fromSearchResults=true&to_year=&rows=100#",
  URL =          "http://www.cs.mun.ca/~tinayu/Publications_files/SPE2005_Final.pdf",
  URL =          "http://www.proceedings.com/00158.html",
  DOI =          "doi:10.2118/95167-MS",
  abstract =     "We have developed a methodology that provides
                 permeability estimates for all rock-types or
                 lithologies, for a wide range of permeability. This is
                 a hybrid Genetic Programming and Fuzzy/Neural Net
                 inference system and which uses lithologic and
                 permeability facies as indicators. This work was
                 motivated by a need to have a volumetric estimate of
                 permeability for reservoir modelling purposes. To this
                 end, for our purposes,the inputs to this process are
                 limited to properties that can be estimated from
                 seismic data. The permeability transform is first
                 estimated at the well locations using core
                 permeability, elastic parameter logs and porosity. The
                 output from the process can then be used, in
                 conjunction with estimates of these properties from 3D
                 seismic data, to provide an estimate of permeability on
                 a volume basis. The inputs are then, the volume of
                 shale (Vsh) or any other log type used to determine
                 lithology, the sonic and density logs, the porosity log
                 and core permeability measurements. The transform
                 system is composed of three distinct modules. The first
                 module serves to classify lithology and separates the
                 reservoir interval into user-defined lithology types.
                 The second module, based on Genetic Programming, is
                 designed to predict permeability facies within
                 lithology type. A permeability facies is defined as as
                 a low, medium or high permeability set associated with
                 each lithology type. A Fuzzy/Neural Net inference
                 algorithm makes up the third module of the system, in
                 which a TSK fuzzy logic relationship is formed, for
                 each permeability facies and lithology.

                 The system has been applied in two oil fields, both
                 offshore West Africa. In comparison with current
                 estimation approaches, this system yields more
                 consistent estimated permeability. The results from
                 conducting cross-validation suggest this methodology is
                 robust in estimating permeability in complex
                 heterogeneous reservoirs.This system is designed to use
                 elastic log properties inverted from seismic data, such
                 as acoustic velocity and density as input so
                 permeability volume can be obtained.",
  notes =        "p3 'In comparison with current estimation approaches,
                 this system yields the estimated permeability that
                 matches core permeability more consistently.'

                 SPE 95167. ChevronTexaco",
}

Genetic Programming entries for Deyi Xie Dave Wilkinson Tina Yu

Citations