Improved facies modelling with multivariate spatial statistics

• Author / Creator

  Li, Yupeng

• This dissertation proposes an improved facies
  modelling methodology that involves a new geological spatial characterization tool, a geological based spatial distance calculation, and a theoretically sound conditional probability calculation. The full set of bivariate probabilities are proposed as a spatial characterization tool that integrates facies stacking information into the final facies model construction. After inference in the vertical direction from well data, they can be transformed to any spatial distance vector based on a heterogeneity
  prototype and the calculation approach proposed in this research. The data information carried by the bivariate probabilities will be integrated together into a multivariate probability based on the minimum Kullback--Leibler distance. From this estimated multivariate probability, the conditional probability for each unsampled location is calculated directly.
  The research developed in this dissertation adds a new
  geostatistical facies modelling approach to currently available tools. It provides a new approach to integrate more geological understanding in the final model. It could be used in practice and as a seed for further development.

• Subjects / Keywords

  • Bivariate probability matrix
  • Sequential simulation
  • Geostatistics
  • Minimum Kullback--Leibler distance
  • Conditional probability
  • Facies modelling
  • Spatial variability characterization

• Graduation date

  Fall 2011

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3FW98

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.