To achieve maximum economic revenues in gas-condensate reservoirs, an optimisation tool is employed to estimate the optimum well placement. Uncertainty analysis in gas-condensate reservoirs is a prerequisite requirement before the developing phase of the hydrocarbon reservoir. Contrary to most conventional reservoir development, well spacing optimisation in gas-condensate fields has received less attention due to a general assumption that optimisation techniques and computational methodologies applied to oil fields development can be applied to gas-condensate fields.

Uncertainty analyses were performed using fourth-order factorial design on a domain of gas-condensate field's data to identify key factors affecting the production of condensates from heterogeneous and ultra-low permeability reservoirs. Well placement objective functions for gas-condensate reservoirs were optimised as functions of cumulative condensate production using genetic algorithms. With compositional modeling, exhaustive search mechanisms were employed to validate the results of our proposed optimisation tool.

Results from the proposed optimisation tool was more economically feasible compared to that of the exhaustive search mechanisms and thus, could be employed as a much simpler, less exhaustive and economically feasible optimisation tool for well placement projects in gas-condensate reservoirs. In using genetic algorithms we concluded that most optimisation tools do not have both reliability and efficiency. Genetic algorithms optimisation tool was observed to be the most reliable method for gas condensate reservoirs though excessive number of simulation runs for large fields makes their application expensive.

A more strategic approach was used to formulate objective functions whilst incorporating the effect of condensate banking in gas-condensate reservoirs.

You can access this article if you purchase or spend a download.