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Analyzing Scaling Characteristics of Transport Properties Using Particle-Tracking Based Techniques

  • Author / Creator
    Vishal, Vikrant
  • Appropriate scale-up provides a critical link between fine-scale heterogeneity descriptions and coarse-scale models used for transport modeling, which is essential for planning and management of subsurface reservoirs. A significant challenge in subsurface flow and transport modeling is to develop scale-appropriate parameters to represent physical heterogeneities that impact solute migration and flow response. Another challenge is to construct reservoir models that would capture the uncertainties stemming from incomplete data (often gathered over different scales) and loss of information or smoothing due to averaging. Fine-scale models contain detailed descriptions of reservoir properties, but these models can be too computationally demanding and are not practically feasible for routine reservoir simulation. Coarse-scale models often offer a viable alternative that could decrease computational demand substantially. However, the increased grid-block size in the coarse scale model leads to an increase in numerical (or artificial) dispersion, which stems from the truncation error from most numerical discretization schemes and is directly proportional to grid-block size. The main issue with numerical dispersion when examining scale-up characteristics is that it tends to overwhelm the physical (or actual) dispersion. Alternative transport modeling schemes, such as the Lagrangian (particle-tracking) methods, are widely adopted in simulating solute transport in porous media. Its primary advantage over typical numerical discretization methods (e.g., finite volume) is the absence of numerical dispersion and potential computational efficiency. More importantly, certain particle-tracking methods are capable of modeling this type of anomalous behavior of transport. In this research, a new particle-tracking method is developed for simulating probabilistic (or random) transition time steps and multi-phase immiscible flow. This is further integrated in a novel hierarchical framework for scale-up of reservoir and transport model parameters including porosity, dispersivity, and multi-phase flow functions (e.g., relative permeability and capillary pressure). A key feature of the developed particle-tracking formulation is the employment of kernel estimator for computing concentration and saturation distribution, which has greatly improved the overall computational efficiency by reducing the number of particles needed to achieve a consistent distribution. The developed particle-tracking method for both probabilistic transition time steps and multi-phase immiscible flow is validated against the analytical solution and is demonstrated to alleviate numerical dispersion when compared against common numerical discretization (e.g. finite difference) methods. Predictions obtained from the coarse-scale models constructed according to the developed workflow are shown to be more consistent with the fine-scale model.

  • Subjects / Keywords
  • Graduation date
    2017-11
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3X63BK44
  • 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.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Civil and Environmental Engineering
  • Specialization
    • Petroleum Engineering
  • Supervisor / co-supervisor and their department(s)
    • Juliana Y. Leung (Petroleum Engineering)
  • Examining committee members and their departments
    • Hassan Dehghanpour (Petroleum Engineering)
    • Clayton Deutsch (Mining Engineering)
    • Japan Trivedi (Petroleum Engineering)
    • Edwin Cey (External, University of Calgary)