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Dynamic Optimization of Smart Oil Well Using Model Predictive Control

Behravan, Hossein | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 47963 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Pishvaie, Mahmoud Reza
  7. Abstract:
  8. In recent years, due to the development of smart wells, optimization of waterflooding by injection/production rate control has receivedsome interests. We can postpone breakthrough time and increase the sweep efficiency by using inflow control valves (ICV). Due to complexity of reservoirs, existence of constraints and numerous influencing parameters, we need a robust and suitable optimization approach to overcome such problems. In this thesis, model predictive control (MPC) is chosen to be our optimization approach. MPC is suitable for constrained multi variable functions. Genetic algorithm is was chosenas optimizer. Eclipse reservoir simulator was used for reservoir simulation. Eclipse input and output files are in text format and this feature helps us to exert our commands to eclipse. We can then read the desired outputs from eclipse in the MATLAB environment. One of the Iranian south fields has been chosen for our case study .A five spot pattern has been considered for this field. Water injection rate was selected as a manipulated variable and some constraints on both rate and pressure. Initially, the case is optimized by considering control and prediction horizon to be 3 and 50, respectively. By justification of obtained results, the prediction horizon was considered to be a variable quantity. The simulation results showed that the optimized waterflooding is much more economical than conventional waterflooding. In other words, such a variable horizon approach is more efficient than the constant prediction horizon
  9. Keywords:
  10. Predictive Control ; Water Flooding ; Genetic Algorithm ; Water Cut ; Smart Wells ; Oil Production

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