Manual generation of test cases and scenario screening processes, during field architecture concept development, may produce a limited number of solutions that do not necessarily lead to an optimal concept selection. For more complex subsea field architectures, which might include processing modules for enhancing pressure and thermal management for the production network, the number of configuration cases and scenarios to evaluate can be extremely large and time and resource-consuming to handle through conventional manual design processes. This paper explores the use of evolutionary algorithms (EA) to automate case generation, scenario screening, and optimization of decentralized subsea processing modules during field development. An evaluation of various genetic operators and evolution strategies was performed to compare their performance and suitability to the application. Based on the evaluation results, an EA using structural uniform crossover and a gradient plus boundary mutation as the main variation operators was developed. The methodology combines EA and an integrated modeling approach to automate and optimize the concept selection and field architecture design when considering decentralized subsea processing modules.

中文翻译：

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支持使用分布式海底处理模块的现场架构方案筛选自动化和优化的进化算法

在现场体系结构概念开发期间，手动生成测试用例和方案筛选过程可能会产生数量有限的解决方案，这些解决方案不一定会导致最佳的概念选择。对于更复杂的海底野外架构，其中可能包括用于增强生产网络的压力和热管理的处理模块，要评估的配置案例和方案的数量可能非常庞大，并且需要通过常规的手动设计流程来处理，这既浪费时间又浪费资源。本文探讨了在田间开发过程中使用进化算法（EA）自动进行案例生成，方案筛选和优化分散式海底处理模块的过程。对各种遗传算子和进化策略进行了评估，以比较它们的性能和对应用的适用性。根据评估结果，开发了一种使用结构均匀交叉和梯度加边界突变作为主要变异算子的EA。该方法结合了EA和集成建模方法，可以在考虑分散式海底处理模块时自动和优化概念选择和现场架构设计。