Closed-loop Reservoir Management Using a Reduced-order Model-based Control & Uncertainty Propagation Framework

该研究奖旨在开发一个精确的降阶模型框架，当与基于模型的最优调节相结合时，将能够改善高维，复杂和非线性分布参数系统的实时控制。本研究的目标包括：（i）开发更精确的计算模型的分布参数系统，将衰减的问题，适应模型和（ii）调查闭环稳定性的降阶模型框架。这些目标将通过一个框架来实现，该框架㈠采用由加速Karhunen-Loève展开产生的特定降阶模型; ㈡应用概率和可能性理论来产生随机模糊变量描述，以说明与不确定变量和参数有关的系统误差和随机误差;（iii）使用有效的分层抽样技术，拉丁超立方体Hammersley抽样，以实现有效的不确定性传播;以及（iv）使用降阶、模型预测、最优控制公式来确定用于未来预测的最优输入。 我们将使用现有的水库和威尔斯作为示范平台，以证明拟议的框架。 预计该框架将通过采用减轻环境损害的方法，并提高我们对如何管理现有和未来油藏和威尔斯的理解，实现油藏和威尔斯的最大生产潜力。 从更广泛的角度来看，精确建模和控制技术的总体研究需求适用于无数其他复杂过程，无论是基于化石的，基于生物的还是其他的。 该奖项将促进研究，教育和工业的重大整合。它将为研究生提供重要的研究课题，并将他们培养为未来有前途的工程师和学者。

This research award is to develop a framework of accurate reduced-order models that when combined with optimal model-based regulation will enable improved real-time control of high dimension, complex and nonlinear distributed parameter systems. The objectives of this research include: (i) develop more accurate computational models of distributed parameter systems that will attenuate the problem of adapting the model and (ii) investigate closed-loop stability of the proposed reduced-order model framework. These objectives will be achieved using a framework that (i) employs a particular reduced-order model generated by an accelerated Karhunen-Loève expansion; (ii) applies both probability and possibility theories to generate a random-fuzzy variable description to account for both systematic and random errors associated with uncertain variables and parameters; (iii) uses an efficient stratified sampling technique, Latin hypercube Hammersley sampling, to enable effective uncertainty propagation; and (iv) determines the optimal inputs for future predictions using a reduced-order, model-predictive, optimal control formulation. We will use existing reservoirs and wells as a demonstration platform to prove out the proposed framework. It is expected that this framework will enable maximum production potential of reservoirs and wells by employing methods that mitigate environmental damage and improve our understanding of how to manage our existing and future reservoirs and wells. From a broader perspective, the overarching research needs of accurate modeling and control technologies are applicable to countless other complex processes whether fossil-based, biological-based, or otherwise. This award will promote a significant integration of research, education and industry. It will provide important research topics to graduate students and educate them as promising engineers and academics in future.