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: Permeability characterization and spatial modeling in complex reservoirs
    sub title: use of tree classifiers and Markov Random Field
  name:
    name part: Perez Vega, Hector H.
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: xx
    date issued: 2002
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    form: remote
    extent: xiv, 112 leaves : illustrations ; 28 cm.
  abstract: This research presents two approaches for working with reservoir properties. The first is the application of decision tree classifiers for predicting partitioning or classifications based on well logs for improving the permeability estimations. The second approach is the application of Markov Random Field (MRF) for determination of a 3D high-resolution model of porosity using well logs and seismic attributes. The prediction of permeability is a critical aspect of reservoir characterization in complex reservoirs such as carbonate reservoirs. In order to improve the permeability estimation in these reservoirs, several techniques have been proposed to partition well log response into different classes, viz., layering or zoning, electrofacies and hydraulic flow units. It has been shown that the partition approaches are quite promising. However, they are generally defined using a specific set of well logs. For this reason, difficulties arise in the estimation of permeability when there are missing well logs. In this research we developed an approach, using the decision tree classifiers, for solving the problem of missing well logs. We predicted the partitions and estimated the impact of missing well logs at two complex carbonate reservoirs in west Texas, viz., Salt Creek Field Unit (SCFU) and North Robertson Unit (NRU). Porosity is another important reservoir property that is obtained from different sources in an oil field; the most common source being well logs. In some reservoirs porosity data from well logs is available only for few wells. Reservoir porosity models built using sparse well information can be highly uncertain. Therefore, we need to integrate information from different scales or sources to get an accurate spatial distribution for porosity. A common source of such additional information is seismic data. There are several techniques available for integrating seismic and well log data e.g., cokriging and its variants. These techniques are reasonably accurat…
  note: by Hector H. Perez Vega.
  note: "Major subject: Petroleum Engineering".
  note: Vita.
  note: Also available online.
  note: Issued also on microfiche from Lange Micrographics.
  note: M.S. Texas A&M University 2002
  note: Includes bibliographical references (leaves 101-106).
  subject:
    topic: Major petroleum engineering
  identifier: https://hdl.handle.net/1969.1/ETD-TAMU-2002-THESIS-P395
  related item:
    location:
      url: https://hdl.handle.net/1969.1/ETD-TAMU-2002-THESIS-P395
  record info:
    record content source: TXA
    record creation date: 030423
    record change date: 20220103114315.0
    record identifier: in00001742759