Closed-loop field development (CLFD) is an exhaustive combination of multidisciplinary tasks to use frequently acquired data for optimizing a pre-defined objective function of the field development plan (FDP). Although new information is bound to decrease the uncertainty around field development, previous studies have shown that CLFD could fail for several theoretical reasons. In this work, a risk-informed CLFD process is introduced to increase the chances of success of the optimized FDP in the true field. A risk-informed CLFD utilizes insights from a systematic approach for evaluating risks associated with field development to make robust decisions for the true field. We implemented the risk-informed CLFD methodology on two different case studies: (I) a scenario with mostly horizontal wells and, (II) a scenario with all vertical wells. While one of the previous studies has shown that CLFD can decrease the NPV by 2% for the presented case study I, our workflow validates the importance of the risk-informed CLFD by improving the net present value (NPV) of the project by 14%. Implementation of CLFD on case study II validates the workflow once again by improving the NPV by 40%. As with previous studies, we considered the project objective function (i.e., NPV) as the key performance indicator of the CLFD. While the performance indicator suffices the requirement of evaluating our methodology, in this work we delved deeper to understand how the intermittently acquired information influences the ensemble of simulation models and uncertainty assessment. We discuss that further fine-tuning the objective function of the optimization problem can improve the likelihood of success in the true field. The paper presents two case studies that are based on a field-scale benchmark model in an attempt to answer the questions about the purport of a field development process with multiple phases while acquiring and utilizing information intermittently. Also, the work validates the risk-informed CLFD methodology to encourage tests on more complex fields. Some key observations to improve the CLFD methodology further are also discussed in the work.

闭环现场开发（CLFD）是多学科任务的详尽组合，可以使用频繁获取的数据来优化现场开发计划（FDP）的预定义目标功能。尽管新的信息势必会减少围绕田间开发的不确定性，但先前的研究表明，CLFD可能由于多种理论原因而失败。在这项工作中，引入了风险告知的CLFD流程，以增加优化的FDP在真实领域中成功的机会。告知风险的CLFD利用系统方法得出的见解来评估与油田开发相关的风险，从而为真实油田做出可靠的决策。我们在两个不同的案例研究中实施了风险告知型CLFD方法：（I）大部分为水平井的场景，（II）全部为垂直井的场景。虽然先前的一项研究表明，对于本案例研究I，CLFD可使NPV降低2％，但我们的工作流程通过将项目的净现值（NPV）提高14％来验证风险告知CLFD的重要性。在案例研究II中实施CLFD可以通过将NPV提高40％再次验证工作流程。与以前的研究一样，我们认为项目目标函数（即NPV）是CLFD的关键绩效指标。尽管性能指标足以评估我们的方法，但在这项工作中，我们更深入地了解了间歇性获取的信息如何影响仿真模型和不确定性评估的整体。我们讨论进一步优化优化问题的目标函数可以提高在实际领域中成功的可能性。本文提出了两个基于实地规模基准模型的案例研究，以期在间歇性获取和利用信息的同时回答有关多阶段实地开发过程的意图的问题。此外，这项工作还验证了基于风险的CLFD方法论，以鼓励在更复杂的领域进行测试。在工作中还讨论了一些进一步改进CLFD方法的关键意见。这项工作验证了基于风险的CLFD方法论，以鼓励在更复杂的领域进行测试。在工作中还讨论了一些进一步改进CLFD方法的关键意见。这项工作验证了基于风险的CLFD方法论，以鼓励在更复杂的领域进行测试。在工作中还讨论了一些进一步改进CLFD方法的关键意见。