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- W2056594198 abstract "Abstract A new optimization technology has been demonstrated for giant oil and gas-condensate fields. Because of the very large number of wells involved, optimization is carried out on well patterns, well spacing in patterns, order of drilling different zones, etc. Earlier developed technology for the optimization of well placement, drilling schedule, well trajectories, and injection strategy has been extended for the optimization of field development schemes involving a very large numbers of wells. The powerful optimization procedure is based on a global optimization technique such as the Genetic Algorithm and other mixed integer optimizers. Potential field development schemes that match field development constraints are identified. A global optimizer is then applied to determine the optimum field development scheme relative to pre-defined economic criteria and analysis. A statistical proxy procedure is utilized to reduce the number of optimization iterations and computing time required to solve this difficult optimization problem. Economic indicators (e.g. net present value, incremental rate of return, capital efficiency, discounted payback, etc.) or oil/gas recovery are maximized subject to field development constraints. The multiple reservoir models representing uncertainties in reservoir geology are handled within the workflow and are represented in predicted results. The applications of this optimization technology is clearly demonstrated to be a powerful and sufficiently general tool for analysis of most real world field development planning issues. Significant improvements (3-13 %) in oil/gas recovery and net present value are demonstrated in the optimum cases when compared with the best manually achieved reference field development case." @default.
- W2056594198 created "2016-06-24" @default.
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- W2056594198 date "2009-02-02" @default.
- W2056594198 modified "2023-09-25" @default.
- W2056594198 title "Field Development Optimization Applied to Giant Oil Fields" @default.
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- W2056594198 doi "https://doi.org/10.2118/118840-ms" @default.
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