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: Integrated reservoir characterization using nonparametric regression and multiscale Markov Random Fields
  name:
    name part: Lee, Sang-Heon.
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: xx
    date issued: 2000
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xiii, 163 leaves : illustrations ; 28 cm.
  abstract: This dissertation presents effective and novel techniques for permeability predictions in uncored wells from well logs using classification and regression, spatial modeling using nonparametric regression, and integrating multiresolution data from a variety of sources into fine scale reservoir models using multiscale Markov Random Field (MRF) for accurate performance forecasting. First, a two-stage approach for permeability predictions from well logs is presented. The main idea of the proposed technique is that by pre-classifying the well log responses into several distinct clusters, or electrofacies (EF) and finding the optimum permeability correlation model for each class using non-parametric regression, more accurate permeability predictions can be obtained. We have used a conventional "unsupervised" pattern recognition technique, model-based clustering, to identify the clusters from well log responses. In predicting permeability at uncored wells, a conventional "supervised" pattern recognition technique, or discriminant analysis was used to find the EF group to which the set of well logs responses at each level is assigned. The field application shows that the proposed technique can improve permeability estimation under highly heterogeneous environments. Second, a nonparametric approach for spatial modeling is presented for integrating well data into reservoir descriptions. Synthetic examples demonstrate the power of Locally Weighted Polynomial Regression (LWPR) as the local trend analysis and field examples illustrate the practical applicability of LWPR for integrating well data into 3-D reservoir model. For stochastic simulations, a hybrid method based on a combination of LWPR and conventional geostatistics is proposed. This method utilizes the advantages of conventional geostatistics such as reproducing the sample data and quantifying uncertainty of the estimates and exploits the power of LWPR for effectively capturing local trend in the spatial data. Third, a…
  note: by Sang Heon Lee.
  note: Vita.
  note: "Major Subject: Petroleum Engineering".
  note: Issued also on microfiche from University Microfilm Inc.
  note: Ph. D. Texas A&M University 2000.
  note: Includes bibliographical references (leaves 139-146).
  subject:
    topic: Major petroleum engineering
  identifier: 99-68954 UMI
  identifier: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  record info:
    record content source: TXA
    record creation date: 010221
    record change date: 20190325101048.0
    record identifier: in00001598854