Management

This paper describes an intelligent completion system in the context of multiple wells that, by electrifying the process, replaces the conventional electrohydraulic systems that have been in use for decades.
In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.
This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty.

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