The Optimization of Gas Allocation to a Group of Wells in a Gas Lift Using an Efficient Ant Colony Algorithm (ACO)

• Published in: Energy sources. Part A, Recovery, utilization, and environmental effects Vol. 36; no. 11; pp. 1234 - 1248
• Main Authors: Ghaedi, M., Ghotbi, C., Aminshahidy, B.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.06.2014
• Subjects: Algorithms; Allocations; ant colony algorithm; Ant colony optimization; Energy use; gas allocation; gas lift; genetic algorithm; Lift; Natural gas; Optimization; production optimization; Wells
• ISSN: 1556-7036; 1556-7230
• DOI: 10.1080/15567036.2010.536829

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.