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- W4309557946 abstract "The optimization of the field development plan involves optimizing the well counts, well type, well locations, and drilling sequence. Traditional approaches for field development optimization have been scenario specific, in which a solution is tied to a particular scenario (e.g., an oil price assumption). In this chapter, generalizable field development optimization is investigated, in which an artificial intelligence trained using a deep reinforcement learning framework can efficiently provide solutions for different scenarios (e.g., a different reservoir/oil price). Compared with previous work on this topic, this work features an improved neural network architecture that allows for a larger template model and accelerates convergence, improved location masking that allows for the incorporation of engineering knowledge, and full integration with a commercial simulator that allows for consideration of practical constraints such as facility capacity. An example application is provided, where an AI is trained for general deepwater Gulf of Mexico reservoirs under primary depletion." @default.
- W4309557946 created "2022-11-28" @default.
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- W4309557946 date "2022-11-22" @default.
- W4309557946 modified "2023-10-18" @default.
- W4309557946 title "Generalizable Field Development Optimization Using Deep Reinforcement Learning with Field Examples" @default.
- W4309557946 doi "https://doi.org/10.1201/9781003207009-20" @default.
- W4309557946 hasPublicationYear "2022" @default.
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