Steam allocation optimization and control for SAGD process

• Author / Creator

  Mohankumar, Yashas

• Steam allocation is an important decision to be made for bitumen thermo-recovery using the Steam Assisted Gravity Drainage (SAGD) technique. This is due to the significant amount of steam requirement and often limited steam generation capacity. Steam-to-oil ratio (SOR) is an important parameter affecting the production performance. It is necessary to address uncertainty in SOR to prevent constraint
  violations in SAGD reservoir states such as subcool and also to maximize the overall steam utilization efficiency. This SAGD steam allocation problem is addressed first, by formulating a NMPC such that uncertainty in SOR is taken into consideration. The allocation is further optimized by managing the development of well-pads and controlling the steam injection to different well-pairs in a given developed well pad.
  The first part of this thesis studies the problem of steam allocation and oil production optimization in the SAGD process considering SOR uncertainty. A first principle model for the SAGD process is developed and further incorporated into the Nonlinear Model Predictive Control (NMPC) problem, which enforces the system to be within various constraints while optimizing an economic objective. The uncertainty is dealt with using three methods in this work: (i) open-loop worst-case optimization, (ii) scenario tree-based closed-loop optimization and (iii) affine policy-based closed-loop optimization. Performances of the above methods are compared through Monte-Carlo simulations. Results demonstrate the superiority of affine policy-based optimization method, which has around 50% improvement of economic performance over static robust and scenario-based method in handling SOR uncertainty.
  Subsequently, we study the problem of integrated well pad development scheduling with nonlinear model predictive control based steam injection in steam-assisted
  gravity drainage (SAGD). The scheduling problem has been modeled as a mixed-integer program to find optimal development sequence and timing of multiple well-pads.
  Model predictive control problems are solved to find optimal steam injection plan such that the reservoir is under control. The integrated problem is solved using open-loop and closed-loop methods: 1) Scheduling problem is only solved at the beginning of project operation, 2) Scheduling problem is solved every year with shrinking horizon implementation, and 3) Shrinking horizon implementation of scheduling with reservoir
  model update based on feedback from control level. Simulation results demonstrate the benefits of closed-loop integrated scheduling and control: the NPV increase is
  18:93%

• Subjects / Keywords

  • Stochastic Optimization
  • Steam allocation
  • Non-linear programming
  • Integrated optimization
  • Model Predictive Control

• Graduation date

  Spring 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-h0gj-wx29

• License

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