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The optimization of gas allocation to a group of wells in a gas lift using an efficient Ant Colony Algorithm (ACO)

Ghaedi, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1080/15567036.2010.536829
  3. Abstract:
  4. 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, using some kind of artificial lift method to provide the energy to bring the fluid to the surface, seems to be necessary. Continuous flow gas lift is one of the most common artificial lift methods widely used in the oil industry during which, at appropriate pressure, gas is injected in a suitable depth into the tubing to gasify the oil column, and thus assist the production. Each well has an optimal point at which it will produce the most oil. In ideal conditions, at which there is no limitation in the total amount of available gas, a sufficient amount of gas could be injected into each well to get the maximum amount of production. However, often the total amount of available gas is insufficient to reach the maximum oil production for each well. Therefore, allocating an optimum amount of gas to each well to obtain field maximum oil production rate is necessary. In this work, a continuous ant colony optimization algorithm was used to allocate the optimum amount of gas to a group of wells for three fields with a different number of wells. Based upon the total production rates of the studied oil fields resulting from the gas allocation to the wells, the continuous ant colony optimization algorithm shows better gas allocation to the wells in comparison with the previous works with other optimization methods
  5. Keywords:
  6. Ant colony algorithm ; Gas allocation ; Gas lift ; Genetic algorithm ; Production optimization ; Ant colony algorithms ; Ant Colony Optimization algorithms ; Artificial lift methods ; Gas allocations ; Oil-production rates ; Optimization method ; Optimization of gas allocation ; Algorithms ; Ant colony optimization ; Gas lifts ; Gases ; Genetic algorithms ; Oil fields ; Water injection ; Well stimulation
  7. Source: Energy Sources, Part A: Recovery, Utilization and Environmental Effects ; Vol. 36, Issue. 11 , 2014 , Pages 1234-1248 ; ISSN: 15567036
  8. URL: http://www.tandfonline.com/doi/abs/10.1080/15567036.2010.536829#.VeKroH01rcs