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Hydrocarbon production from a gas condensate reservoir has a major concern to the petroleum industry around the world. Due to pressure decline in a reservoir, liquid drops out of solution and cause a significant reduction of well productivity. As a result, huge amount of valuable oil stays underground. The proper production management for a gas condensate reservoir minimizes or eliminates the blocking problems. Optimization of condensate recovery can be achieved through appropriate selection of production and injection rates. However, determination of the optimal rates is a complex problem, involving many factors including geological uncertainty, rock petrophysical characteristics, fluid properties, economic costs, and technical ability. In this research, nature-inspired optimization algorithms are employed to evaluate the potential of production boost. Numerical testing and comparative study revealed that investigated natural-inspired algorithms outperform widely reported in the literature optimization methods and provide a higher quality solutions. The primary concern of this research is to develop an optimal gas condensate production strategy in terms of economic efficiency by utilization of nature-inspired optimization algorithm and providing their ability to generate better quality solution than engineering approach as well as gradient optimization.

Utilization of nature-inspired algorithms for gas condensate reservoir optimization