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: Data integration into high resolution reservoir models using geostatistics and multiscale Markov Random Fields
  name:
    name part: Malallah, Adel Hussain
    name part: 1969-
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: xx
    date issued: 2001
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xv, 130 leaves : illustrations ; 28 cm.
  abstract: Integrating multiresolution data into reservoir models for accurate performance forecasting is a longstanding challenge in reservoir characterization. Important information about the reservoir rocks scan different length scales of heterogeneity and have different degrees of precision. Data from core plugs, well logs, and seismic testing must all be included in the construction of a geological reservoir model. Hence, spatial models that are defined over a hierarchy of scales are required to integrate these data. This study developed two different techniques to integrate different reservoir data that have different scales and precision. The first technique uses nonparametric transformation and non-Gaussian cosimulation to infer water saturation distribution in the interwell region by using the relationship between seismic velocity and formation resistivity. We have applied our methodology to a synthetic model as well as a field example. The synthetic example validates the approach and involves reproducing a pre-generated primary data set using sparse primary and dense secondary data. The field example uses cross-well seismic velocity and well data from the Buena Vista Hills field, a fractured siliceous shale reservoir in California. The second data integration technique is a hierarchical approach to spatial modeling based on Markov Random Fields (MRF) and multiresolution algorithms in image analysis. Being locally specific provides several advantages: (a) MRFs are computationally tractable and ideally suited to simulation-based computation such as MCMC (Markov Chain Monte Carlo) methods, and (b) model extensions to account for nonstationary, discontinuity, and varying spatial properties are easily accessible in the MRF framework. Our proposed method is computationally efficient and well-suited to reconstruct fine-scale spatial fields from coarser, multiscale samples (e.g., based on seismic and production data) and sparse fine scale conditioning data (e.g., well data).…
  note: by Adel Hussain Malallah.
  note: Vita.
  note: "Major Subject: Petroleum Engineering".
  note: Issued also on microfiche from University Microfilm Inc.
  note: Ph. D. Texas A&M University 2001.
  note: Includes bibliographical references (leaves 112-118).
  subject:
    topic: Major petroleum engineering
  identifier: 30-20082 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: 020515
    record change date: 20190325103117.0
    record identifier: in00001675771