Chance-Constrained Model Predictive Control for SAGD Process Using Robust Optimization Approximation,Industrial & Engineering Chemistry Research

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Chance-Constrained Model Predictive Control for SAGD Process Using Robust Optimization Approximation
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2018-11-13 , DOI: 10.1021/acs.iecr.8b03207
Wenhan Shen ₁ , Zukui Li ₁ , Biao Huang ₁ , Nabil Magbool Jan ₁

Affiliation

Control of a steam-assisted gravity drainage (SAGD) process is a challenging task, because of the presence of various uncertainties, such as geological uncertainty and steam quality uncertainty. They often lead to constraint violations and performance degradation. In this work, a chance-constrained model predictive control (CCMPC) method is presented to generate a safe and optimal control strategy, considering the presence of uncertainties. A novel robust optimization method is applied to solve the chance-constrained optimization problem under general distribution of uncertainties. Two case studies are presented to demonstrate the proposed approach. Furthermore, the modeling of SAGD process is discussed, and the proposed robust optimization-based CCMPC is tested using a reservoir simulator (Petroleum Experts) of the SAGD process. The proposed approach reduces constraint violations that are due to uncertainties and achieves satisfactory performance.

中文翻译：

基于鲁棒优化逼近的SAGD过程机会约束模型预测控制

由于存在各种不确定性，例如地质不确定性和蒸汽质量不确定性，因此控制蒸汽辅助重力排水（SAGD）过程是一项艰巨的任务。它们经常导致违反约束和性能下降。在这项工作中，考虑了不确定性的存在，提出了一种机会约束模型预测控制（CCMPC）方法，以生成安全和最佳的控制策略。提出了一种新颖的鲁棒优化方法来解决不确定性一般分布下的机会约束优化问题。提出了两个案例研究，以证明所提出的方法。此外，讨论了SAGD过程的建模，并使用SAGD过程的油藏模拟器（石油专家）对建议的基于鲁棒优化的CCMPC进行了测试。

更新日期：2019-05-23

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