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: Prediction of reservoir properties of the N-sand, vermilion block 50, Gulf of Mexico, from multivariate seismic attributes
  name:
    name part: Jaradat, Rasheed Abdelkareem
    name part: 1970-
    role:
      role term: creator
  name:
    name part: Watkins, Joel S.
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: remote
  abstract: The quantitative estimation of reservoir properties directly from seismic data is a major goal of reservoir characterization. Integrated reservoir characterization makes use of different varieties of well and seismic data to construct detailed spatial estimates of petrophysical and fluid reservoir properties. The advantage of data integration is the generation of consistent and accurate reservoir models that can be used for reservoir optimization, management and development. This is particularly valuable in mature field settings where hydrocarbons are known to exist but their exact location, pay, lateral variations and other properties are poorly defined. Recent approaches of reservoir characterization make use of individual seismic attributes to estimate inter-well reservoir properties. However, these attributes share a considerable amount of information among them and can lead to spurious correlations. An alternative approach is to evaluate reservoir properties using multiple seismic attributes. This study reports the results of an investigation of the use of multivariate seismic attributes to predict lateral reservoir properties of gross thickness, net thickness, gross effective porosity, net-to-gross ratio and net reservoir porosity thickness product. This approach uses principal component analysis and principal factor analysis to transform eighteen relatively correlated original seismic attributes into a set of mutually orthogonal or independent PC's and PF's which are designated as multivariate seismic attributes. Data from the N-sand interval of Vermilion Block 50 field, Gulf of Mexico, was used in this study. Multivariate analyses produced eighteen PC's and three PF's grid maps. A collocated cokriging geostaistical technique was used to estimate the spatial distribution of reservoir properties of eighteen wells penetrating the N-sand interval. Reservoir property maps generated by using multivariate seismic attributes yield highly accurate predictions of rese…
  note: by Rasheed Abdelkareem Jaradat.
  note: "Major Subject: Geophysics"
  note: Title from author supplied metadata (automated record created on Sep. 21, 2005.)
  note: Vita.
  note: Abstract.
  note: Electronic resource.
  note: Mode of access: World Wide Web.
  note: System requirements: World Wide Web access and Adobe Acrobat Reader.
  note: Ph. D. Texas A&M University 2004.
  note: Includes bibliographical references.
  note: Text (Dissertation).
  subject:
    topic: Major Geophysics
  subject:
    topic: Principal factor analysis
  subject:
    topic: Net reservoir porosity thickness product
  subject:
    topic: Multiple seismic attributes
  subject:
    topic: Gross thickness
  subject:
    topic: Gross effective porosity
  subject:
    topic: Gulf Of Mexico
  subject:
    topic: Seismic attributes
  subject:
    topic: Miocene.
  subject:
    topic: Net thickness
  subject:
    topic: Reservoir property mapping
  subject:
    topic: Multivariate seismic attributes
  subject:
    topic: Integrated reservoir characterization
  subject:
    topic: Vermilion Block 50
  subject:
    topic: Net-to-gross ratio
  subject:
    topic: Principal component analysis
  identifier: http://hdl.handle.net/1969.1/2236
  record info:
    record content source: TXA
    record creation date: 070118
    record change date: 20151104123804.0
    record identifier: in00002208889