rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Well log correlation using artificial intelligence
  name:
    name part: Kuo, Tsai-Bao
    name part: 1954-
    role:
      role term: creator
  name:
    name part: Berg, R. R.
    role:
      role term: degree committee member.
  name:
    name part: Lee, W. J.
    role:
      role term: degree committee member.
  name:
    name part: Morgan, S. M.
    role:
      role term: degree committee member.
  name:
    name part: Painter, J. H.
    role:
      role term: degree committee member.
  name:
    name part: Russell, J. E.
    role:
      role term: degree committee member.
  name:
    name part: Scartzman, Richard A.
    role:
      role term: degree supervisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  genre: Academic theses
  origin info:
    place:
      place term: xx
    date issued: 1986
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: x, 138 leaves : illustrations ; 29 cm
  abstract: Computer-assisted approaches to well log correlation are of interest to petroleum engineers and geologists for two reasons, in large field studies a computer can be used to simply reduce the time required to correlate zones of interest. It is also possible that computer-assisted correlations may suggest zonal matches of interest and originality that night not have been considered. The objective of this study is to develop a new approach to computer-assisted log correlations. The new approach is based on a zonal correlation, methodology and an artificial intelligence technique -- rule-based systems. A prototype rule-based computer software system is developed to implement this method. The system correlates logs in a fashion parallel to human experts. With this system, correlations are directly derived from the shapes of log traces. Results obtained from applying field data show that this new approach can correlate distinct geological horizons with a high degree of success.
  note: "Major subject: Petroleum Engineering."
  note: Typescript (photocopy).
  note: Vita.
  note: Ph. D. Texas A & M University 1986
  note: Includes bibliographical references (leaves 93-96).
  subject:
    topic: Oil well logging
    topic: Data processing
  subject:
    topic: Petroleum
    topic: Geology
    topic: Data processing
  subject:
    topic: Major petroleum engineering
  identifier: http://proxy.library.tamu.edu/login
  identifier: https://hdl.handle.net/1969.1/DISSERTATIONS-450168
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: https://hdl.handle.net/1969.1/DISSERTATIONS-450168
  record info:
    record content source: TXA
    record creation date: 870511
    record change date: 20220103135021.0
    record identifier: in00000443050
    language of cataloging:
      language term: eng