Reservoir Geological Uncertainty Reduction and Its Applications in Reservoir Development Optimization

  • Author / Creator
    Rahim, Shahed
  • Three different studies related to geological uncertainty reduction in reservoir applications are performed in this thesis. The first study proposes an optimal realization reduction framework for quantifying geological uncertainty. The second study applies the optimal realization reduction framework to incorporate geological uncertainty into an application of vertical well placement optimization. The third study proposes a two stage Steam Assisted Gravity Drainage (SAGD) well drainage area (DA) arrangement optimization method and incorporates geological uncertainty to the SAGD arrangement optimization by using the optimal realization reduction framework. Geological uncertainty is reduced by generating and incorporating multiple realizations in the application of reservoir development or controls optimization. However, only few realizations are selected from a large superset for the reservoir application due to intensive computational efforts. The proposed optimal realization reduction method is a mixed integer linear optimization model which minimizes the probability distance between the discrete distribution represented by the superset of realizations and the reduced discrete distribution represented by the selected realizations. The results of applying the realization reduction method to various case studies show that the proposed method can effectively select realizations and assign probabilities such that the extreme and expected reservoir performances are recovered better than any of the other realization reduction methods. The optimal realization reduction method is then used to select a subset of realizations and incorporate them into a framework of robust vertical well placement optimization under geological uncertainty. Applying the well placement optimization framework to reservoirs demonstrate the similarity between the expected reservoir performance results from well placement optimization using the realization reduction method and well placement optimization using all the realizations in the superset. SAGD is an increasingly popular in-situ method for extraction of bitumen from Alberta’s oil sands. The first stage of the model determines the optimal arrangement of the compact set of all the DAs that maximize the available bitumen. The second stage of the model selects a smaller set of DA and surface pad (SP) from the compact arrangement that maximize the available bitumen and minimizes the distance between the selected SPs. Results of applying the SAGD well arrangement optimization method to a reservoir lease area showed a compact DA arrangement with DAs containing higher bitumen content and SPs in close proximity to each other being selected. Geological uncertainty is incorporated to the optimization method by using selected realizations obtained from the optimal realization reduction framework. Results showed DA arrangement plan with higher expected bitumen and greater number of DAs within the compact arrangement.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • 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
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Chemical and Materials Engineering
  • Specialization
    • Chemical Engineering
  • Supervisor / co-supervisor and their department(s)
    • Li, Zukui (Chemical and Materials Engineering)
  • Examining committee members and their departments
    • Trivedi, Japan (Civil and Environmental Engineering)
    • Zeng, Hongbo (Chemical and Materials Engineering)
    • Prasad, Vinay (Chemical and Materials Engineering)
    • Li, Zukui (Chemical and Materials Engineering)