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Robust optimization of the production profile of the steam assisted gravity drainage reservoir using a polynomial chaos expansion-based proxy model

机译:使用基于多项式混沌扩展的代理模型对蒸汽辅助重力排水油藏生产剖面进行稳健的优化

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Uncertainty quantification is essential to ensure robustly stable operation in many engineering applications. Steam assisted gravity drainage (SAGD) reservoirs are large scale distributed parameter dynamical systems and pose significant challenges in quantifying the uncertainty. An effective data driven method to develop computationally inexpensive proxy models for the dynamic outputs and their uncertainty would be valuable for optimization and control. In this work, we present a proxy model developed using polynomial chaos expansion (PCE) for the cumulative oil production (COP) of a SAGD reservoir at different steam injection rates. The proxy model is used in robust optimization using statistical metrics of COP calculated over the entire parameter space. An ensemble of realizations of the cumulative oil production is simulated corresponding to the ensemble of the petro-physical properties over which the PCE model coefficients are estimated using collocation points over the orthogonal polynomial basis under an inner product relationship. Robust optimization is performed as a trade-off between maximum nominal performance and robustness and used to find the optimum steam injection rate for maximum oil production with minimum variability.
机译:不确定度量化对于确保在许多工程应用中的稳定运行至关重要。蒸汽辅助重力排水(SAGD)油藏是大规模的分布式参数动力学系统,在量化不确定性方面提出了重大挑战。为动态输出及其不确定性开发计算上廉价的代理模型的有效数据驱动方法,对于优化和控制很有价值。在这项工作中,我们提出了使用多项式混沌扩展(PCE)为SAGD油藏在不同注汽速率下的累计采油量(COP)开发的代理模型。代理模型用于鲁棒优化,使用在整个参数空间上计算的COP的统计指标。对应于石油物理性质的集合,模拟了累积采油量的实现集合,在该集合集合中,在内部积关系下,使用正交多项式上的搭配点来估计PCE模型系数。进行鲁棒性优化是在最大标称性能和鲁棒性之间进行权衡,并用于找到最佳的蒸汽注入速率,从而以最小的可变性获得最大的采油量。

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