An efficient approach for optimizing full field development plan using Monte-Carlo simulation coupled with Genetic Algorithm and new variable setting method for well placement applied to gas condensate field in Vietnam

This paper presents an efficient technique to optimize a gas condensate field development plan under economic uncertainties. Many studies have been conducted to optimize development plan but mostly limited to oil field under fixed economic environments and required huge number of simulation runs. It is proved that black oil model can be a reasonable alternative of compositional model to complete field development optimization within acceptable period when reservoir pressure is higher enough than dew point pressure. This study implements Monte-Carlo simulation to Genetic Algorithm to assess economic uncertainties while optimization procedure is being performed and to avoid duplicating whole optimization procedure by changing economic assumptions. An idea for setting optimization variables for well placement is also introduced to reduce required number of simulation runs. A real field application confirms that the technique can be applied to optimize a gas condensate field with contractual gas sales obligation, and the idea plays a key role to find the optimized solution with limited resources by reducing the number of simulation runs required during the optimization procedure. The proposed technique can be applied to optimize not only full field development plan but also reservoir management plan and it will be helpful to improve economics of all kinds of E&P projects under lots of uncertainties.

Abstract licence: CC BY

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