Field development and control optimization aim to maximize the economic profit of oil and gas production while respecting various constraints such as production limits imposed by the available fluid processing capacity and/or reservoir management strategies. The limitations of the existing optimization workflows are 1) well locations or production/injection controls are optimized independently despite the fact that one affects another, and 2) forthcoming field production limits are ignored during at least one of these optimization stages. This paper presents a robust, multi-level framework for field development and control optimization under fluid processing capacity constraints while considering reservoir description uncertainty.

The developed framework is based on sequential iterative optimization of control variables at different levels, where the loop of well placement followed by control optimization continues until no significant improvement is observed in the expected objective value. Simultaneous perturbation stochastic approximation (SPSA) algorithm is employed as the optimizer at all optimization levels. Field production constraints are imposed on the reservoir model using a simplified production network. Smart clustering techniques are applied to systematically select an ensemble of reservoir model realizations as the representative of all available realizations at each optimization level.

The developed framework is tested on the Brugge benchmark field case study with a maximum field liquid production limit imposed via the production network. A comparative analysis is performed for each case to investigate the impact of field liquid production constraints on optimal well placement and control strategy. Results demonstrate that ignoring fluid processing capacity constraints, in one or multiple levels of optimization, results in a sub-optimal scenario, highlighting the significance of the proposed optimization framework in robust field development and management.

油田开发和控制优化旨在最大程度地提高油气生产的经济效益，同时遵守各种约束条件，例如可用流体处理能力和/或储层管理策略所施加的生产极限。现有优化工作流程的局限性是：1）尽管一个事实会影响另一个因素，但对井位或生产/注入控制进行了独立优化，并且2）在这些优化阶段中的至少一个阶段中，忽略了即将到来的田间生产极限。本文提出了一个健壮的，多层次的框架，用于在流体处理能力约束下进行油田开发和控制优化，同时考虑了储层描述的不确定性。

所开发的框架基于不同级别的控制变量的顺序迭代优化，在此过程中，先进行井布置再进行控制优化的循环，直到未观察到预期目标值有明显改善为止。在所有优化级别上，均采用同步摄动随机逼近（SPSA）算法作为优化器。使用简化的生产网络，对油田模型施加了现场生产约束。应用智能聚类技术来系统地选择一组油藏模型实现，作为每个优化级别上所有可用实现的代表。

所开发的框架已在布鲁日基准现场案例研究中进行了测试，并通过生产网络施加了最大现场液体生产限制。针对每种情况进行了比较分析，以调查现场液体生产限制因素对最佳井位布置和控制策略的影响。结果表明，在一个或多个优化级别中忽略流体处理能力约束会导致次优方案，从而突出说明了所建议的优化框架在稳健的油田开发和管理中的重要性。