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Optimization of Gas Allocation to a Group of Wells in Gas Lift

Ghaedi, Mojtaba | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 41038 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Ghotbi, Siroos; Amin Shahidi, Babak
  7. Abstract:
  8. When the reservoir energy is too low for the well to flow, or the production rate desired is greater than the reservoir energy can deliver, it becomes necessary to put the well on some form of artificial lift to provide the energy to bring the fluid to the surface. Continuous flow gas lift is one of the most common artificial lift methods widely used in the oil industry. In continuous gas lift, gas at high-pressure is injected in a suitable depth into the tubing, to gasify the oil column, and thus facilitate the production. Every well has an optimal gas lift operating point at which it will produce the most fluid. Ideally, if there is no restriction in the total amount of gas available, sufficient gas could be injected into an individual well until maximum production is achieved. However, in most cases, the total amount of injection gas volume available for the system of wells is insufficient to reach the maximum oil production for every well. Therefore, it is necessary to assign the injection gas into each well in optimal form to obtain the field maximum oil production rate. In this project two algorithms (Hybrid genetic algorithm (HGA) and continuous ant colony (CACO) algorithm) were used for optimum allocation of gas. Optimum gas allocations with these two algorithms were done for some fields with different number of wells. Total rate resulted from two introduced algorithms in fields under study, are more than those of previous works with other methods. In a field with 6 wells gas allocation with both HGA and CACO, resulted in 9.3 STB/day increase in field total production rate in comparison to best result of previous works. In a field with 56 wells, HGA gas allocation resulted in 2742 STB/day increase in field total production rate and CACO gas allocation resulted in 2742.7 STB/day increase in field total production rate in comparison to best result of previous works. So introduced algorithms are more efficient in gas allocation
  9. Keywords:
  10. Genetic Algorithm ; Gas Lift ; Ant Colony Algorithm ; Production Optimization ; Gas Allocation ; Artificial Lift

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