Multivariate geostatistical modeling of unconventional shale gas reservoirs in the presence of sparse data

• Author / Creator

  Hmoud, Samer

• A geostatistical workflow for modeling multivariate sparsely sampled variables in shale gas reservoirs is proposed in this thesis and applied to a study area in the HRB. This workflow accounts for direct and cross spatial correlation between variables while decreases computational modeling time by aggregating secondary variables into super secondary variables that help in generating more accurate models for primary variables.

  Parameter uncertainty such as histogram and variogram uncertainty are investigated. Histogram uncertainty is incorporated in the final geostatistical model by calculating prior histogram uncertainty using multivariate SB on conditional data and transferring this uncertainty to simulation engine that is updated by conditioning and model domain extents. The study results show that histogram uncertainty incorporation adds a significant amount of uncertainty to the generated geostatistical models if compared with simulation uncertainty using fixed histogram.

  Variogram uncertainty is incorporated in this study using variogram realizations generated using the DoF method in which each variogram realization is standardized and used to simulate one simulation realization. Moreover, uncertainty in variogram is improved using secondary-derived variogram approach which relies on having exhaustive secondary data. The study results show that variogram uncertainty does not provide a significant change in modeling uncertainty when compared to the uncertainty of fixed variogram model simulation.

  The principle of SSS is introduced in this thesis in which geological sweet spots are identified by first selecting and then ordering key variables according to their importance. Percentile cutoffs are then chosen for all variables in all realizations. Finally, the probability of a cell to be classified as a geological sweet spot is calculated for all cells in the model. The results show that some areas in Muskwa Formation and Evie Member are classified as high-probable high-quality reservoir rocks based on density porosity, total organic carbon, and brittleness values when compared to Otter Park Member, and these areas can be visually inspected and identified in the study area.

• Subjects / Keywords

  • Parameter Uncertainty
  • Sparse Data
  • Multivariate
  • Sweet Spots
  • Horn River Shale
  • Shale Gas
  • Geostatistics

• Graduation date

  Fall 2018

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3ZK56304

• License

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